Distinguishing between seismic and aseismic fault slip in the geologic record is difficult, yet fundamental to estimating the seismic potential of faults and the likelihood of multi-fault ruptures. We integrated chirp sub-bottom imaging with targeted cross-fault coring and core analyses of sedimentary proxy data to characterize vertical deformation and slip behavior within an extensional fault bend along the Hayward-Rodgers Creek fault system in northern San Pablo Bay. We identified and traced four key seismic horizons (R1–R4), all younger than approximately 1400 CE, that cross the fault and extend throughout the basin. A stratigraphic age model was developed using detailed down-core radiocarbon and radioisotope dating combined with measurements of anthropogenic metal concentrations. The onset of hydraulic mining within the Sierra Nevada in 1852 CE left a clear geochemical and magnetic signature within core samples. This key time horizon was used to calculate a local reservoir correction and reduce uncertainty in radiocarbon age calibration and models. Vertical fault offset of strata younger than the most recent surface-rupturing earthquake on the Hayward fault in 1868 CE suggest near-surface vertical creep is occurring along the fault in northern San Pablo Bay at a rate of approximately 0.4 mm/yr. In addition, we present evidence of at least one and possibly two coseismic events associated with growth strata above horizons R1 and R2, with median event ages estimated to be 1400 CE and 1800 CE, respectively. The timing of both these events overlaps with paleoseismic events on adjacent fault sections, suggesting the possibility of multi-fault rupture.
The Eyjafjallajökull eruption of 2010 demonstrated the far-reaching impact of ash clouds and the vulnerability of our jet-based society to them, prompting a review of procedures to detect, warn, and forecast ash cloud hazards to aviation. The years since 2010 have seen marked improvements in satellite technology, more accurate ash-dispersion models that integrate simulations with observations, and more thoroughly vetted and harmonized procedures for forecasting and communicating hazards. In the coming decade, we expect these trends to continue, with formal warning products migrating from text and simplified maps to 4D data streams. This paper details some of these advancements and challenges ahead.
The 2019 Ridgecrest, California, earthquake sequence involved predominantly right‐lateral strike slip on a northwest–southeast‐trending subvertical fault in the 6 July M 7.1 mainshock, preceded by left‐lateral strike slip on a northeast–southwest‐trending subvertical fault in the 4 July M 6.4 foreshock. To characterize the postseismic deformation, we assemble displacements measured by Global Positioning System (GPS) and Interferometric Synthetic Aperture Radar. The geodetic measurements illuminate vigorous postseismic deformation for at least 21 months following the earthquake sequence. The postseismic transient deformation is particularly well constrained from survey‐mode GPS (sGPS) in the epicentral region carried out during the weeks after the mainshock. We interpret these observations with mechanical models including afterslip and viscoelastic relaxation of the lower crust and mantle asthenosphere. During the first 21 months, up to several centimeters of horizontal motions are measured at continuous GPS and sGPS sites, with amplitude that diminishes slowly with distance from the mainshock rupture, suggestive of deeper afterslip or viscoelastic relaxation. We find that although afterslip involving right‐lateral strike slip along the mainshock fault traces and their deeper extensions reach a few decimeters, most postseismic deformation is attributable to viscoelastic relaxation of the lower crust and mantle. Within the Basin and Range crust and mantle, we infer a transient lower crust viscosity several times that of the mantle asthenosphere. The transient mantle asthenosphere viscosity is
We consider the future of volcano observatories in a world where new satellite technologies and global data initiatives have greatly expanded over the last two decades. Observatories remain the critical tie between the decision-making authorities and monitoring data. In the coming decade, the global scientific community needs to continue to collaborate in a manner that will strengthen volcano observatories while building those databases and scientific models that allow us to improve forecasts of eruptions and mitigate their impacts. Observatories in turn need to contribute data to allow these international collaborations to prosper.
For the sea otter (Enhydra lutris), genetic population structure is an area of research that has not received significant attention, especially in Southwest Alaska where that distinct population segment has been listed as threatened since 2005 pursuant to the U.S. Endangered Species Act. In this study, 501 samples from 14 locations from Prince William Sound, Alaska to the Commander Islands in Russia were analyzed for variation at 13 microsatellite loci. Our results indicate a high degree of genetic divergence among the 14 locations (FST = 0.120) with gene flow conforming to the isolation by distance (IBD) model (r2 = 0.491, p < .05). The 14 sampling locations formed six geographic associations in clustering and ordination analyses that likely correspond to remnant population lineages: (1) Southcentral Alaska, (2) Kodiak and North Alaska Peninsula, (3) South Alaska Peninsula and Bristol Bay, (4) Eastern Aleutian, (5) Western Aleutian, and (6) the Commander Islands. Except for South Alaska Peninsula and Bristol Bay, these clusters closely agree with previously defined stock and management unit boundaries. Our results reveal significant genetic population structure and are generally congruent with current management strategies for the threatened Southwest Alaska distinct population segment.
Epikarst estuary response to hydroclimate change remains poorly understood, despite the well-studied link between climate and karst groundwater aquifers. The influence of sea-level rise and coastal geomorphic change on these estuaries obscures climate signals, thus requiring careful development of paleoenvironmental histories to interpret the paleoclimate archives. We used foraminifera assemblages, carbon stable isotope ratios (δ13C) and carbon:nitrogen (C:N) mass ratios of organic matter in sediment cores to infer environmental changes over the past 5300 years in Celestun Lagoon, Yucatan, Mexico. Specimens (> 125 µm) from modern core top sediments revealed three assemblages: (1) a brackish mangrove assemblage of agglutinated Miliammina and Ammotium taxa and hyaline Haynesina (2) an inner-shelf marine assemblage of Bolivina, Hanzawaia, and Rosalina, and (3) a brackish assemblage dominated by Ammonia and Elphidium. Assemblages changed along the lagoon channel in response to changes in salinity and vegetation, i.e. seagrass and mangrove. In addition to these three foraminifera assemblages, lagoon sediments deposited since 5300 cal yr BP are comprised of two more assemblages, defined by Archaias and Laevipeneroplis, which indicate marine Thalassia seagrasses, and Trichohyalus, which indicates restricted inland mangrove ponds. Our data suggest that Celestun Lagoon displayed four phases of development: (1) an inland mangrove pond (5300 BP) (2) a shallow unprotected coastline with marine seagrass and barrier island initiation (4900 BP) (3) a protected brackish lagoon (3000 BP), and (4) a protected lagoon surrounded by mangroves (1700 BP). Stratigraphic (temporal) changes in core assemblages resemble spatial differences in communities across the modern lagoon, from the southern marine sector to the northern brackish region. Similar temporal patterns have been reported from other Yucatan Peninsula lagoons and from cenotes (Nichupte, Aktun Ha), suggesting a regional coastal response to sea level rise and climate change, including geomorphic controls (longshore drift) on lagoon salinity, as observed today. Holocene barrier island development progressively protected the northwest Yucatan Peninsula coastline, reducing mixing between seawater and rain-fed submarine groundwater discharge. Superimposed on this geomorphic signal, assemblage changes that are observed reflect the most severe regional wet and dry climate episodes, which coincide with paleoclimate records from lowland lake archives (Chichancanab, Salpeten). Our results emphasize the need to consider coastal geomorphic evolution when using epikarst estuary and lagoon sediment archives for paleoclimate reconstruction and provide evidence of hydroclimate changes on the Yucatan Peninsula.
Demand for critical raw materials is expected to accelerate over the next few decades due to continued population growth and the shifting consumption patterns of the global economy. Sedimentary basins are important sources for critical raw materials and new discoveries of sediment–hosted Mississippi Valley–type (MVT) and/or clastic–dominated (CD) Zn–Pb deposits are likely required to mitigate future supply chain disruptions for Zn, Pb, Ag, Cd, Ga, Ge, Sb, and In. Herein we integrate public geoscience datasets using a discrete global grid to system to model the mineral potential for MVT and CD deposits across Canada, the United States of America, and Australia. Statistical analysis of the model results demonstrates that surface–wave tomography and derivative products from satellite gravity datasets can be used to map the most favourable paleo–tectonic settings of MVT and CD deposits inboard of orogenic belts and at the rifted edges of cratonic lithosphere, respectively. Basin development at pre–existing crustal boundaries was likely important for maintaining the low geothermal–gradients that are favourable for metal transport and generating the crustal fluid pathways that were reactivated during ore–formation, as suggested by the statistical association of both sediment–hosted mineral deposit types with the edges of upward–continued gravity and long–wavelength magnetic anomalies. Multivariate statistical analysis demonstrates that the most prospective combination of these geophysical datasets varies for each geological region and deposit type. We further demonstrate that maximum and minimum geological ages, coupled with Phanerozoic paleogeographic reconstructions, represent mappable proxies for the availability of oxidized, brine–generating regions that are the most likely source of ore–forming fluids (e.g., low– to mid–latitude carbonate platforms and evaporites). Ore deposition was likely controlled by interaction between oxidized, low–temperature brines and sulfidic and/or carbonaceous rocks, which, in some cases, can be mapped at the exposed surface or identified using the available rock descriptions. Baseline weights–of–evidence models are based on regional geophysics and are the least impacted by missing surface information but yield relatively poor results, as demonstrated by the low area–under–the–curve (AUC) for the spatially independent test set on the success–rate plot (AUC = 0.787 for MVT and AUC = 0.870 for CD). Model performance can be improved by: (1) using advanced methods that were trained and validated during a series of semi–automated machine learning competitions; and/or (2) incorporating geological and geophysical datasets that are proxies for each component of the mineral system. The best–performing gradient boosting machine models yield higher AUC for the test set (AUC = 0.983 for MVT and AUC = 0.991 for CD) and reduce the search space by >94%. The model results highlight the potential benefits of mapping sediment–hosted mineral systems at continental scale to improve mineral exploration targeting for critical raw materials.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.oregeorev.2021.104635","usgsCitation":"Lawley, C.J., McCafferty, A.E., Graham, G.E., Huston, D.L., Kelley, K.D., Czarnota, K., Paradis, S., Peter, J.M., Hayward, N., Barlow, M., Emsbo, P., Coyan, J.A., San Juan, C.A., and Gadd, M.G., 2022, Data-driven prospectivity modelling of sediment-hosted Zn-Pb mineral systems and their critical raw materials: Ore Geology Reviews, v. 141, p. 1-23, https://doi.org/10.1016/j.oregeorev.2021.104635.","productDescription":"104635, 23 p.","startPage":"1","endPage":"23","ipdsId":"IP-132045","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":449402,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.oregeorev.2021.104635","text":"Publisher Index Page"},{"id":436028,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P970GDD5","text":"USGS data release","linkHelpText":"National-Scale Geophysical, Geologic, and Mineral Resource Data and Grids for the United States, Canada, and Australia: Data in Support of the Tri-National Critical Minerals Mapping Initiative"},{"id":393050,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Australia, Canada, United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -125.68359374999999,\n 47.754097979680026\n ],\n [\n -124.98046874999999,\n 45.706179285330855\n ],\n [\n -125.68359374999999,\n 42.4234565179383\n ],\n [\n -124.98046874999999,\n 39.095962936305476\n ],\n [\n -123.04687499999999,\n 34.88593094075317\n ],\n [\n -117.94921874999999,\n 32.24997445586331\n ],\n [\n -116.54296874999999,\n 32.54681317351514\n ],\n [\n -112.32421875,\n 30.751277776257812\n ],\n [\n -107.40234375,\n 31.50362930577303\n ],\n [\n -104.23828125,\n 28.613459424004414\n ],\n [\n -102.3046875,\n 29.38217507514529\n ],\n [\n -97.91015624999999,\n 25.48295117535531\n ],\n [\n -96.15234375,\n 27.68352808378776\n ],\n [\n -92.63671875,\n 28.613459424004414\n ],\n [\n -84.55078125,\n 28.613459424004414\n ],\n [\n -82.08984375,\n 24.206889622398023\n ],\n [\n -79.453125,\n 25.48295117535531\n ],\n [\n -80.68359375,\n 30.90222470517144\n ],\n [\n -75.41015624999999,\n 34.59704151614417\n ],\n [\n -74.70703125,\n 37.71859032558816\n ],\n [\n -69.9609375,\n 40.44694705960048\n ],\n [\n -69.78515625,\n 42.5530802889558\n ],\n [\n -66.97265625,\n 44.08758502824516\n ],\n [\n -66.09375,\n 42.4234565179383\n ],\n [\n -59.765625,\n 45.213003555993964\n ],\n [\n -59.94140624999999,\n 47.040182144806664\n ],\n [\n -53.0859375,\n 45.460130637921004\n ],\n [\n -51.50390625,\n 46.92025531537451\n ],\n [\n -55.1953125,\n 50.28933925329178\n ],\n [\n -54.31640625,\n 52.802761415419674\n ],\n [\n -59.23828124999999,\n 56.17002298293205\n ],\n [\n -64.86328125,\n 61.270232790000634\n ],\n [\n -67.8515625,\n 59.17592824927136\n ],\n [\n -67.67578124999999,\n 60.58696734225869\n ],\n [\n -72.24609375,\n 62.59334083012024\n ],\n [\n -78.57421875,\n 62.99515845212052\n ],\n [\n -78.57421875,\n 59.977005492196\n ],\n [\n -79.8046875,\n 58.44773280389084\n ],\n [\n -77.6953125,\n 56.17002298293205\n ],\n [\n -80.33203125,\n 55.7765730186677\n ],\n [\n -80.5078125,\n 52.3755991766591\n ],\n [\n -81.38671875,\n 55.677584411089526\n ],\n [\n -92.28515625,\n 58.35563036280964\n ],\n [\n -92.98828125,\n 60.84491057364912\n ],\n [\n -88.59374999999999,\n 63.39152174400882\n ],\n [\n -78.92578124999999,\n 61.270232790000634\n ],\n [\n -79.453125,\n 63.54855223203644\n ],\n [\n -82.44140625,\n 65.44000165965534\n ],\n [\n -81.03515625,\n 66.65297740055279\n ],\n [\n -76.81640625,\n 66.44310650816469\n ],\n [\n -80.5078125,\n 64.62387720204688\n ],\n [\n -76.46484375,\n 63.78248603116502\n ],\n [\n -66.62109375,\n 61.10078883158897\n ],\n [\n -64.16015624999999,\n 62.2679226294176\n ],\n [\n -59.94140624999999,\n 66.99884379185184\n ],\n [\n -66.97265625,\n 68.65655498475735\n ],\n [\n -66.09375,\n 69.09993967425089\n ],\n [\n -66.796875,\n 70.8446726342528\n ],\n [\n -73.125,\n 72.18180355624855\n ],\n [\n -76.11328125,\n 73.82482034613932\n ],\n [\n -96.50390625,\n 74.4021625984244\n ],\n [\n -101.07421875,\n 74.21198251594369\n ],\n [\n -103.53515625,\n 73.62778879339942\n ],\n [\n -106.69921875,\n 74.01954331150228\n ],\n [\n -111.4453125,\n 73.02259157147301\n ],\n [\n -114.2578125,\n 73.87371654457475\n ],\n [\n -120.76171875,\n 74.913708156753\n ],\n [\n -125.5078125,\n 74.59010800882325\n ],\n [\n -125.68359374999999,\n 73.67726447634907\n ],\n [\n -127.265625,\n 71.91088787611527\n ],\n [\n -123.92578125,\n 70.67088107015755\n ],\n [\n -119.88281249999999,\n 71.07405646336098\n ],\n [\n -117.7734375,\n 69.41124235697256\n ],\n [\n -127.61718749999999,\n 70.78690984117928\n ],\n [\n -129.375,\n 70.37785394109224\n ],\n [\n -130.95703125,\n 70.55417853776078\n ],\n [\n -136.58203125,\n 69.28725695167886\n ],\n [\n -142.3828125,\n 70.37785394109224\n ],\n [\n -150.82031249999997,\n 70.8446726342528\n ],\n [\n -157.32421875,\n 71.80141030136785\n ],\n [\n -166.46484375,\n 69.09993967425089\n ],\n [\n -167.87109375,\n 68.33437594128185\n ],\n [\n -162.7734375,\n 66.37275500247455\n ],\n [\n -165.58593749999997,\n 66.99884379185184\n ],\n [\n -168.92578125,\n 65.73062649311031\n ],\n [\n -167.34375,\n 64.24459476798195\n ],\n [\n -162.24609375,\n 63.93737246791484\n ],\n [\n -165.234375,\n 63.470144746565424\n ],\n [\n -166.640625,\n 62.186013857194226\n ],\n [\n -168.3984375,\n 59.62332522313024\n ],\n [\n -162.421875,\n 58.26328705248601\n ],\n [\n -158.73046875,\n 57.98480801923985\n ],\n [\n -166.11328125,\n 54.97761367069628\n ],\n [\n -165.05859375,\n 53.74871079689897\n ],\n [\n -156.09375,\n 56.65622649350222\n ],\n [\n -154.68749999999997,\n 56.17002298293205\n ],\n [\n -152.2265625,\n 56.84897198026975\n ],\n [\n -151.171875,\n 57.98480801923985\n ],\n [\n -149.23828125,\n 59.62332522313024\n ],\n [\n -146.25,\n 59.712097173322924\n ],\n [\n -141.6796875,\n 59.265880628258095\n ],\n [\n -137.8125,\n 57.70414723434193\n ],\n [\n -134.296875,\n 53.014783245859235\n ],\n [\n -130.078125,\n 50.51342652633956\n ],\n [\n -125.68359374999999,\n 47.754097979680026\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 134.12109375,\n -34.74161249883172\n ],\n [\n 144.66796875,\n -43.96119063892024\n ],\n [\n 148.18359375,\n -44.087585028245165\n ],\n [\n 151.5234375,\n -36.03133177633187\n ],\n [\n 154.3359375,\n -31.503629305773018\n ],\n [\n 154.68749999999997,\n -25.48295117535531\n ],\n [\n 147.48046875,\n -17.978733095556155\n ],\n [\n 143.26171875,\n -10.141931686131018\n ],\n [\n 141.50390625,\n -10.141931686131018\n ],\n [\n 139.5703125,\n -16.299051014581817\n ],\n [\n 136.58203125,\n -14.604847155053898\n ],\n [\n 136.93359375,\n -11.350796722383672\n ],\n [\n 131.1328125,\n -10.487811882056683\n ],\n [\n 128.49609375,\n -13.923403897723334\n ],\n [\n 125.859375,\n -12.554563528593656\n ],\n [\n 120.41015624999999,\n -17.811456088564473\n ],\n [\n 112.5,\n -21.616579336740593\n ],\n [\n 112.32421875,\n -25.799891182088306\n ],\n [\n 114.43359375,\n -32.54681317351514\n ],\n [\n 113.37890625,\n -34.45221847282653\n ],\n [\n 114.9609375,\n -36.03133177633187\n ],\n [\n 130.78125,\n -32.54681317351514\n ],\n [\n 134.12109375,\n -34.74161249883172\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"141","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Lawley, Christopher J. M.","contributorId":270169,"corporation":false,"usgs":false,"family":"Lawley","given":"Christopher","email":"","middleInitial":"J. M.","affiliations":[{"id":13092,"text":"Geological Survey of Canada","active":true,"usgs":false}],"preferred":false,"id":828591,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCafferty, Anne E. 0000-0001-5574-9201 anne@usgs.gov","orcid":"https://orcid.org/0000-0001-5574-9201","contributorId":1120,"corporation":false,"usgs":true,"family":"McCafferty","given":"Anne","email":"anne@usgs.gov","middleInitial":"E.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":828592,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Graham, Garth E. 0000-0003-0657-0365 ggraham@usgs.gov","orcid":"https://orcid.org/0000-0003-0657-0365","contributorId":1031,"corporation":false,"usgs":true,"family":"Graham","given":"Garth","email":"ggraham@usgs.gov","middleInitial":"E.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":828593,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Huston, David L.","contributorId":270173,"corporation":false,"usgs":false,"family":"Huston","given":"David","email":"","middleInitial":"L.","affiliations":[{"id":35920,"text":"Geoscience Australia","active":true,"usgs":false}],"preferred":false,"id":828595,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kelley, Karen D. 0000-0002-3232-5809 kdkelley@usgs.gov","orcid":"https://orcid.org/0000-0002-3232-5809","contributorId":179012,"corporation":false,"usgs":true,"family":"Kelley","given":"Karen","email":"kdkelley@usgs.gov","middleInitial":"D.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":828596,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Czarnota, Karol","contributorId":259291,"corporation":false,"usgs":false,"family":"Czarnota","given":"Karol","affiliations":[{"id":35920,"text":"Geoscience Australia","active":true,"usgs":false}],"preferred":false,"id":828597,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Paradis, Suzanne","contributorId":259282,"corporation":false,"usgs":false,"family":"Paradis","given":"Suzanne","affiliations":[{"id":13092,"text":"Geological Survey of Canada","active":true,"usgs":false}],"preferred":false,"id":828598,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Peter, Jan M.","contributorId":270175,"corporation":false,"usgs":false,"family":"Peter","given":"Jan","email":"","middleInitial":"M.","affiliations":[{"id":13092,"text":"Geological Survey of Canada","active":true,"usgs":false}],"preferred":false,"id":828599,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hayward, Nathan","contributorId":270177,"corporation":false,"usgs":false,"family":"Hayward","given":"Nathan","email":"","affiliations":[{"id":13092,"text":"Geological Survey of Canada","active":true,"usgs":false}],"preferred":false,"id":828600,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Barlow, Mike","contributorId":270179,"corporation":false,"usgs":false,"family":"Barlow","given":"Mike","email":"","affiliations":[{"id":35920,"text":"Geoscience Australia","active":true,"usgs":false}],"preferred":false,"id":828601,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Emsbo, Poul 0000-0001-9421-201X pemsbo@usgs.gov","orcid":"https://orcid.org/0000-0001-9421-201X","contributorId":997,"corporation":false,"usgs":true,"family":"Emsbo","given":"Poul","email":"pemsbo@usgs.gov","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":828602,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Coyan, Joshua Aaron 0000-0002-8450-7364","orcid":"https://orcid.org/0000-0002-8450-7364","contributorId":247291,"corporation":false,"usgs":true,"family":"Coyan","given":"Joshua","email":"","middleInitial":"Aaron","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":828603,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"San Juan, Carma A. 0000-0002-9151-1919 csanjuan@usgs.gov","orcid":"https://orcid.org/0000-0002-9151-1919","contributorId":1146,"corporation":false,"usgs":true,"family":"San Juan","given":"Carma","email":"csanjuan@usgs.gov","middleInitial":"A.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":828604,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Gadd, Michael G.","contributorId":270171,"corporation":false,"usgs":false,"family":"Gadd","given":"Michael","email":"","middleInitial":"G.","affiliations":[{"id":13092,"text":"Geological Survey of Canada","active":true,"usgs":false}],"preferred":false,"id":828594,"contributorType":{"id":1,"text":"Authors"},"rank":14}]}} ,{"id":70226875,"text":"70226875 - 2022 - Using fish community and population indicators to assess the biological condition of streams and rivers of the Chesapeake Bay watershed, USA","interactions":[],"lastModifiedDate":"2021-12-20T12:06:31.635434","indexId":"70226875","displayToPublicDate":"2021-12-16T08:59:49","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"Using fish community and population indicators to assess the biological condition of streams and rivers of the Chesapeake Bay watershed, USA","docAbstract":"The development of indicators to assess relative freshwater condition is critical for management and conservation. Predictive modeling can enhance the utility of indicators by providing estimates of condition for unsurveyed locations. Such approaches grant understanding of where “good” and “poor” conditions occur and provide insight into landscape contexts supporting such conditions. However, as assessments are conducted at large extents crossing jurisdictional boundaries, combined datasets are likely not suited for traditional assessment approaches which rely on jurisdictionally-specific reference sites. Here, we used a large dataset compiled from multiple providers to assess the condition of fish habitat for non-tidal streams and rivers in the Chesapeake Bay watershed (CBW), USA. We concurrently used community and species-level analyses to provide a more holistic view of habitat conditions by using random forest models to predict selected metrics and species occurrence with landscape data for inland CBW stream reaches. Community analyses included metrics describing composition, tolerances, habitat preferences, and functional traits of fish communities whereas species-level analyses consisted of distribution models for key sensitive and gamefish species. For community analyses, a final index was calculated as the average of selected metric deciles with higher scores inferring less biologically altered (i.e., better) conditions, providing an alternative to using reference sites. For species analyses, species occurrence was predicted for stream reaches, with presence indicating suitable habitat. Uncertainty was calculated for both approaches using model prediction intervals. Results indicated different numbers of suitable metrics for each region, with most in the Northern Appalachian (15) and least in the Southern Appalachian Piedmont (3). Four species (three sensitive) were suitable for modeling. At the CBW scale, predictions did not vary greatly among deciles for the community or species analyses for 2001, 2006, 2011, and 2016. Most stream reaches did not vary in mean decile rank or in species occurrence between 2001 and 2016; however, the largest community changes occurred in large rivers in the Coastal Plains ecoregion and the largest species occurrence changes occurred in Torrent Suckers in medium-sized rivers. When compared, results from community analyses agreed for one sensitive species (Brook Trout) but not the other three, potentially due to regionally inappropriate tolerance assignment. Comparisons also demonstrated substantial variation among approaches suggesting a lack of redundancy. While each approach traditionally has its targeted audience and respective strengths and weaknesses, concurrent use of these approaches permits direct comparisons and may assuage shortcomings of each approach when considered separately.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2021.108488","usgsCitation":"Maloney, K.O., Krause, K.P., Cashman, M.J., Daniel, W., Gressler, B.P., Wieferich, D.J., and Young, J.A., 2022, Using fish community and population indicators to assess the biological condition of streams and rivers of the Chesapeake Bay watershed, USA: Ecological Indicators, v. 134, 108488, 17 p., https://doi.org/10.1016/j.ecolind.2021.108488.","productDescription":"108488, 17 p.","ipdsId":"IP-133787","costCenters":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true},{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":449408,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2021.108488","text":"Publisher Index Page"},{"id":393049,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Chesapeake Bay watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.1904296875,\n 38.41916639395372\n ],\n [\n -75.223388671875,\n 38.64261790634527\n ],\n [\n -75.35522460937499,\n 38.79690830348427\n ],\n [\n -75.498046875,\n 38.87392853923629\n ],\n [\n -75.5419921875,\n 39.0533181067413\n ],\n [\n -75.662841796875,\n 39.30029918615029\n ],\n [\n -75.750732421875,\n 39.70718665682654\n ],\n [\n -75.6298828125,\n 40.052847601823984\n ],\n [\n -75.69580078125,\n 40.07807142745009\n ],\n [\n -75.95947265625,\n 40.052847601823984\n ],\n [\n -76.0693359375,\n 40.069664523297774\n ],\n [\n -76.058349609375,\n 40.18726672309203\n ],\n [\n -75.9375,\n 40.29628651711716\n ],\n [\n -75.91552734375,\n 40.3549167507906\n ],\n [\n -75.89355468749999,\n 40.47202439692057\n ],\n [\n -76.09130859375,\n 40.56389453066509\n ],\n [\n -76.190185546875,\n 40.64730356252251\n ],\n [\n -76.0693359375,\n 40.75557964275589\n ],\n [\n -75.83862304687499,\n 40.871987756697415\n ],\n [\n -75.76171875,\n 40.91351257612758\n ],\n [\n -75.706787109375,\n 40.95501133048621\n ],\n [\n -75.7177734375,\n 41.071069130806414\n ],\n [\n -75.662841796875,\n 41.1455697310095\n ],\n [\n -75.5419921875,\n 41.13729606112276\n ],\n [\n -75.322265625,\n 41.104190944576466\n ],\n [\n -75.377197265625,\n 41.22824901518529\n ],\n [\n -75.377197265625,\n 41.28606238749825\n ],\n [\n -75.377197265625,\n 41.43449030894922\n ],\n [\n -75.399169921875,\n 41.6154423246811\n ],\n [\n -75.34423828125,\n 41.68111756290652\n ],\n [\n -75.2783203125,\n 41.91045347666418\n ],\n [\n -75.38818359375,\n 42.00848901572399\n ],\n [\n -75.377197265625,\n 42.09007006868398\n ],\n [\n -75.223388671875,\n 42.17968819665961\n ],\n [\n -74.970703125,\n 42.26917949243506\n ],\n [\n -74.8388671875,\n 42.32606244456202\n ],\n [\n -74.520263671875,\n 42.415346114253616\n ],\n [\n -74.278564453125,\n 42.54498667313236\n ],\n [\n -74.322509765625,\n 42.64204079304426\n ],\n [\n -74.410400390625,\n 42.80346172417078\n ],\n [\n -74.68505859374999,\n 42.924251753870685\n ],\n [\n -75.069580078125,\n 42.98053954751642\n ],\n [\n -75.38818359375,\n 42.96446257387128\n ],\n [\n -75.684814453125,\n 42.93229601903058\n ],\n [\n -75.9375,\n 42.87596410238256\n ],\n [\n -76.201171875,\n 42.827638636242284\n ],\n [\n -76.26708984375,\n 42.72280375732727\n ],\n [\n -76.2890625,\n 42.601619944327965\n ],\n [\n -76.2890625,\n 42.52069952914966\n ],\n [\n -76.343994140625,\n 42.415346114253616\n ],\n [\n -76.46484375,\n 42.382894009614034\n ],\n [\n -76.640625,\n 42.431565872579185\n ],\n [\n -76.7724609375,\n 42.39912215986002\n ],\n [\n -76.80541992187499,\n 42.24478535602799\n ],\n [\n -76.88232421875,\n 42.285437007491545\n ],\n [\n -76.9482421875,\n 42.415346114253616\n ],\n [\n -77.04711914062499,\n 42.44778143462245\n ],\n [\n -77.14599609375,\n 42.415346114253616\n ],\n [\n -77.2998046875,\n 42.382894009614034\n ],\n [\n -77.222900390625,\n 42.54498667313236\n ],\n [\n -77.442626953125,\n 42.69858589169842\n ],\n [\n -77.574462890625,\n 42.60970621339408\n ],\n [\n -77.640380859375,\n 42.48830197960227\n ],\n [\n -77.728271484375,\n 42.439674178149424\n ],\n [\n -77.6513671875,\n 42.31793945446847\n ],\n [\n -77.596435546875,\n 42.22851735620852\n ],\n [\n -77.5634765625,\n 42.09007006868398\n ],\n [\n -77.6953125,\n 41.92680320648791\n ],\n [\n -77.9150390625,\n 41.83682786072714\n ],\n [\n -78.0908203125,\n 41.795888098191426\n ],\n [\n -78.453369140625,\n 41.599013054830216\n ],\n [\n -78.453369140625,\n 41.50857729743935\n ],\n [\n -78.42041015625,\n 41.376808565702355\n ],\n [\n -78.3984375,\n 41.21172151054787\n ],\n [\n -78.519287109375,\n 41.054501963290505\n ],\n [\n -78.541259765625,\n 40.9218144123785\n ],\n [\n -78.409423828125,\n 40.713955826286046\n ],\n [\n -78.299560546875,\n 40.55554790286311\n ],\n [\n -78.343505859375,\n 40.48873742102282\n ],\n [\n -78.475341796875,\n 40.30466538259176\n ],\n [\n -78.64013671875,\n 40.06125658140474\n ],\n [\n -78.826904296875,\n 39.9434364619742\n ],\n [\n -78.848876953125,\n 39.80853604144591\n ],\n [\n -78.85986328125,\n 39.715638134796336\n ],\n [\n -78.99169921875,\n 39.69873414348139\n ],\n [\n -79.046630859375,\n 39.64799732373418\n ],\n [\n -79.266357421875,\n 39.436192999314095\n ],\n [\n -79.420166015625,\n 39.2832938689385\n ],\n [\n -79.354248046875,\n 39.26628442213066\n ],\n [\n -79.266357421875,\n 39.232253141714885\n ],\n [\n -79.2333984375,\n 39.155622393423215\n ],\n [\n -79.244384765625,\n 39.01918369029134\n ],\n [\n -79.27734374999999,\n 38.89103282648846\n ],\n [\n -79.398193359375,\n 38.74551518488265\n ],\n [\n -79.661865234375,\n 38.54816542304656\n ],\n [\n -79.683837890625,\n 38.47079371120379\n ],\n [\n -79.727783203125,\n 38.34165619279595\n ],\n [\n -79.815673828125,\n 38.20365531807149\n ],\n [\n -80.04638671875,\n 38.013476231041935\n ],\n [\n -80.17822265625,\n 37.779398571318765\n ],\n [\n -80.2880859375,\n 37.59682400108367\n ],\n [\n -80.4638671875,\n 37.47485808497102\n ],\n [\n -80.694580078125,\n 37.38761749978395\n ],\n [\n -80.771484375,\n 37.23032838760387\n ],\n [\n -80.57373046875,\n 37.26530995561875\n ],\n [\n -80.44189453125,\n 37.309014074275915\n ],\n [\n -80.255126953125,\n 37.31775185163688\n ],\n [\n -80.013427734375,\n 37.3002752813443\n ],\n [\n -79.8486328125,\n 37.23907530202184\n ],\n [\n -79.771728515625,\n 37.18657859524883\n ],\n [\n -79.6728515625,\n 37.07271048132943\n ],\n [\n -79.541015625,\n 37.09900294387622\n ],\n [\n -79.354248046875,\n 37.142803443716836\n ],\n [\n -79.1455078125,\n 37.10776507118514\n ],\n [\n -79.112548828125,\n 37.055177106660814\n ],\n [\n -78.936767578125,\n 36.932330061503144\n ],\n [\n -78.837890625,\n 36.94111143010769\n ],\n [\n -78.662109375,\n 37.055177106660814\n ],\n [\n -78.486328125,\n 37.03763967977139\n ],\n [\n -78.42041015625,\n 36.94111143010769\n ],\n [\n -78.20068359374999,\n 36.96744946416934\n ],\n [\n -77.904052734375,\n 37.03763967977139\n ],\n [\n -77.750244140625,\n 37.081475648860525\n ],\n [\n -77.53051757812499,\n 37.081475648860525\n ],\n [\n -77.354736328125,\n 37.07271048132943\n ],\n [\n -77.069091796875,\n 37.081475648860525\n ],\n [\n -76.959228515625,\n 37.01132594307015\n ],\n [\n -76.893310546875,\n 36.932330061503144\n ],\n [\n -76.871337890625,\n 36.83566824724438\n ],\n [\n -76.849365234375,\n 36.677230602346214\n ],\n [\n -76.7724609375,\n 36.527294814546245\n ],\n [\n -76.629638671875,\n 36.55377524336089\n ],\n [\n -76.46484375,\n 36.589068371399115\n ],\n [\n -76.35498046875,\n 36.48314061639213\n ],\n [\n -76.256103515625,\n 36.57142382346277\n ],\n [\n -76.190185546875,\n 36.66841891894786\n ],\n [\n -76.0693359375,\n 36.65079252503471\n ],\n [\n -75.9375,\n 36.66841891894786\n ],\n [\n -75.948486328125,\n 36.76529191711624\n ],\n [\n -75.904541015625,\n 37.01132594307015\n ],\n [\n -75.926513671875,\n 37.17782559332976\n ],\n [\n -75.882568359375,\n 37.42252593456307\n ],\n [\n -75.618896484375,\n 37.640334898059486\n ],\n [\n -75.509033203125,\n 37.82280243352756\n ],\n [\n -75.38818359375,\n 38.013476231041935\n ],\n [\n -75.16845703124999,\n 38.272688535980976\n ],\n [\n -75.1904296875,\n 38.41916639395372\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"134","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Maloney, Kelly O. 0000-0003-2304-0745 kmaloney@usgs.gov","orcid":"https://orcid.org/0000-0003-2304-0745","contributorId":4636,"corporation":false,"usgs":true,"family":"Maloney","given":"Kelly","email":"kmaloney@usgs.gov","middleInitial":"O.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":828570,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Krause, Kevin P. 0000-0002-0255-7027","orcid":"https://orcid.org/0000-0002-0255-7027","contributorId":218454,"corporation":false,"usgs":true,"family":"Krause","given":"Kevin","email":"","middleInitial":"P.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":828571,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cashman, Matthew J. 0000-0002-6635-4309","orcid":"https://orcid.org/0000-0002-6635-4309","contributorId":203315,"corporation":false,"usgs":true,"family":"Cashman","given":"Matthew","middleInitial":"J.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":828572,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Daniel, Wesley M. 0000-0002-7656-8474","orcid":"https://orcid.org/0000-0002-7656-8474","contributorId":219320,"corporation":false,"usgs":true,"family":"Daniel","given":"Wesley M.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":828573,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gressler, Benjamin P. 0000-0001-6639-8558","orcid":"https://orcid.org/0000-0001-6639-8558","contributorId":270167,"corporation":false,"usgs":true,"family":"Gressler","given":"Benjamin","middleInitial":"P.","affiliations":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"preferred":true,"id":828574,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wieferich, Daniel J. 0000-0003-1554-7992 dwieferich@usgs.gov","orcid":"https://orcid.org/0000-0003-1554-7992","contributorId":176205,"corporation":false,"usgs":true,"family":"Wieferich","given":"Daniel","email":"dwieferich@usgs.gov","middleInitial":"J.","affiliations":[{"id":5069,"text":"Office of the AD Core Science Systems","active":true,"usgs":true},{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":828575,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Young, John A. 0000-0002-4500-3673 jyoung@usgs.gov","orcid":"https://orcid.org/0000-0002-4500-3673","contributorId":3777,"corporation":false,"usgs":true,"family":"Young","given":"John","email":"jyoung@usgs.gov","middleInitial":"A.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":828576,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70228764,"text":"70228764 - 2022 - Multimineral petrophysics of thermally immature Eagle Ford Group and Cretaceous mudstones, U.S. Geological Survey Gulf Coast 1 research wellbore in central Texas","interactions":[],"lastModifiedDate":"2022-02-18T13:26:34.530633","indexId":"70228764","displayToPublicDate":"2021-12-14T07:23:41","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3906,"text":"Interpretation","active":true,"publicationSubtype":{"id":10}},"title":"Multimineral petrophysics of thermally immature Eagle Ford Group and Cretaceous mudstones, U.S. Geological Survey Gulf Coast 1 research wellbore in central Texas","docAbstract":"Traditional petrophysical methods to evaluate organic richness and mineralogy using gamma-ray and resistivity log responses are not diagnostic in source rocks. We have developed a deterministic, nonproprietary method to quantify formation variability in total organic carbon (TOC) and three key mudrock mineralogical components of nonhydrocarbon-bearing source rock strata of the Eagle Ford Group by developing a set of log-derived multimineral models calibrated with Fourier transform infrared spectroscopy core data from the research borehole U.S. Geological Survey Gulf Coast 1 West Woodway. We determined that bulk density response is a reliable indicator of organic content in these thermally immature, water-bearing source rocks. Multimineral findings indicate that a high degree of laminae-scale mineralogical heterogeneity exists due to thinly interbedded carbonate cements amid clay-rich mudstone layers. The lower part of the Eagle Ford Group contains the highest average TOC content (4.7 wt%) and the highest average carbonate volume (64.1 vol%), making it the optimal target in thermally mature areas for source-rock potential and hydraulic-fracture placement. In contrast, the uppermost portion of the Eagle Ford Group contains the highest average volume of clay minerals (42.6 vol%), which increases the potential for wellbore stability issues. Petrophysical characterization reveals that porosity is approximately 30% in this relatively uncompacted formation. In this thermally immature source rock, water saturation is nearly 100% and no free hydrocarbons were observed on the resistivity logs. No evidence of borehole ellipticity was observed on the three-arm caliper log, and horizontal stresses are presumed to be directionally uniform in the vicinity of this near-surface wellbore. This shallow wellbore has a temperature gradient of 1.87°F/100 ft (16.3°C/km) and is likely influenced by earth surface heating.
Long distance, post-release movements of translocated wildlife can be a key factor limiting translocation success. Yet, for many species, we have little or no understanding of factors that influence post-release movements. Translocations have been important for recovering fisher Pekania pennanti populations across the southern portion of their North American range. However, little is known about the post-release movements of translocated fishers and how these movements may be influenced by demographic or translocation-process factors. To restore fishers in Washington State, we moved 90 fishers from central British Columbia and released them at nine sites in the Olympic Fisher Recovery Area on the Olympic Peninsula of Washington from 2008 to 2010. We evaluated post-release movements of 48 fishers to determine both the distance and duration of movements prior to home range establishment. Fishers moved extensively following their release. Multi-model selection indicated a high level of support for the hypothesis that post-release movements differed by fisher sex and age; whereas, year of release had no apparent effect on movements, and release date had only a marginal influence on male movements. Mean distance (± 95% CI) from a release site to a home range was greater for adult males (62.0 ± 19.6 km) than for juvenile males (31.4 ± 16.0 km), adult females (30.9 ± 21.1 km), and juvenile females (29.0 ± 13.5 km). Mean number of days from release until home range establishment was similar for the sexes, however the variance in movement duration was greater for females. Twenty-six of 27 females established home ranges over an 11-month period (December-October), while 19 of 21 males did so within a 4-month period (April-July). Mean home range sizes differed between males (128.3 ± 21.1 km2) and females (63.5 ± 9.0 km2) and were among the largest reported for the species. A greater proportion of females (18 of 27; 67%) than males (8 of 21; 38%) established home ranges within or partially within the recovery area. Six females left a previously established home range during the breeding season, presumably to find breeding males. Given the large distances that fishers can move following release, translocation success could be furthered by releasing individuals at fewer sites in the interior of large reintroduction areas to facilitate greater exposure to a recovery area and greater opportunity to interact with conspecifics and potential mates.
Ensemble-based data assimilation (DA) methods have displayed strong potential to improve model state and parameter estimation across several disciplines due to their computational efficiency, scalability, and ability to estimate uncertainty in the dynamic states and the parameters. However, a barrier to adoption of ensemble DA methods remains. Namely, there is currently a lack of available tools that enable efficient and scalable DA in a non-intrusive fashion and that support implementation flexibility. This paper presents an open-source software tool (PESTPP-DA) that implements a range of data assimilation methods—Ensemble Kalman filter, Ensemble Kalman Smoother and Ensemble Smoother—using the widely known PEST model-interface protocols, to interact with any model. Two iterative solutions can be used for nonlinear and/or non-Gaussian assimilation problems. To demonstrate the broad range of PESTPP-DA applications, two synthetic case studies are presented: (1) the Lorenz model and (2) a groundwater pumping test in the presence of a non-Gaussian hydraulic conductivity field.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2021.105284","usgsCitation":"Alzraiee, A.H., White, J., Knowling, M., Hunt, R., and Fienen, M., 2022, A scalable model-independent iterative data assimilation tool for sequential and batch estimation of high dimensional model parameters and states: Environmental Modelling & Software, v. 150, 105284, 13 p., https://doi.org/10.1016/j.envsoft.2021.105284.","productDescription":"105284, 13 p.","ipdsId":"IP-135009","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":397702,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"150","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Alzraiee, Ayman H. 0000-0001-7576-3449","orcid":"https://orcid.org/0000-0001-7576-3449","contributorId":272120,"corporation":false,"usgs":true,"family":"Alzraiee","given":"Ayman","email":"","middleInitial":"H.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":838898,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"White, Jeremy T. 0000-0002-4950-1469","orcid":"https://orcid.org/0000-0002-4950-1469","contributorId":248830,"corporation":false,"usgs":false,"family":"White","given":"Jeremy T.","affiliations":[{"id":50032,"text":"GNS New Zealand","active":true,"usgs":false}],"preferred":false,"id":838899,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Knowling, Matthew 0000-0002-7273-3495","orcid":"https://orcid.org/0000-0002-7273-3495","contributorId":251904,"corporation":false,"usgs":false,"family":"Knowling","given":"Matthew","email":"","affiliations":[{"id":36277,"text":"GNS Science","active":true,"usgs":false}],"preferred":false,"id":838900,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hunt, Randall J. 0000-0001-6465-9304","orcid":"https://orcid.org/0000-0001-6465-9304","contributorId":16118,"corporation":false,"usgs":true,"family":"Hunt","given":"Randall J.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":838901,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fienen, Michael N. 0000-0002-7756-4651","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":245632,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael N.","affiliations":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"preferred":true,"id":838902,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70226895,"text":"70226895 - 2022 - Parameterizing an aeolian erosion model for rangelands","interactions":[],"lastModifiedDate":"2021-12-20T12:28:08.433345","indexId":"70226895","displayToPublicDate":"2021-12-13T06:26:13","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":666,"text":"Aeolian Research","active":true,"publicationSubtype":{"id":10}},"title":"Parameterizing an aeolian erosion model for rangelands","docAbstract":"Aeolian processes are fundamental to arid and semi-arid ecosystems, but modeling approaches are poorly developed for assessing impacts of management and environmental change on sediment transport rates over meaningful spatial and temporal scales. For model estimates to provide value, estimates of sediment flux that encapsulate intra- and inter-annual and spatial variability are needed. Further, it is important to quantify and communicate transparent estimates of model uncertainty to users. Here, we present a wind erosion and dust emission model parameterized for rangelands using a Generalized Likelihood Uncertainty Estimation framework. Modeled horizontal sediment flux was calibrated using data from five diverse grassland and shrubland sites from the USDA National Wind Erosion Research Network. Observations of wind speed, vegetation height, length of gaps between vegetation, and percent bare ground were used as model inputs. Horizontal sediment flux estimates from 10,000 independently selected parameter sets were compared to flux observations from 44 ∼ month-long collection periods to calculate a likelihood measure for each model. Results show good agreement for individual sampling periods across sites with few observations falling outside prediction bounds and a one-to-one relationship between median predictions and observations. Additionally, combined distributions of sediment flux estimates from all sample periods for a given site closely approximated the probability of observing a given flux at that site. These results suggest AERO effectively represents temporal variability in aeolian transport rates at rangeland sites and provides robust assessments suitable for assessing land health and better predicting changes in air quality and the impacts of land management activities.
Angler tag-return studies are a cornerstone of fisheries research, providing insights into individual movements and estimates of exploitation, among many other applications. However, the data generated from these studies is dependent upon effective communication between anglers and scientists. As technological advances are adopted by anglers, little research has been directed at the potential benefits of incorporating modern tag reporting methods. We tagged stream-dwelling black bass Micropterus spp. and provided anglers a choice of reporting methods (telephone, email, iNaturalist app, or a “mixed-mode” combination thereof). Our objectives were to examine the fate of reported fish, quantify trends in data quality across reporting methods, and explore how geographic location and angler avidity may influence use of reporting methods. Ninety-four percent of tag reports involved the release of the fish with the tag still intact, creating opportunities for longer-term data collection. Telephone was the most commonly used reporting method; however, this method had significantly lower completeness scores (e.g., lack of photographs or specifying fate of fish) and less precise location information than other methods. In contrast, iNaturalist had the highest completeness and most precise location information but was seldom used and had increased lag times in reporting. We found no significant differences in the proportion of reporting methods used across stream locations in our study, and avid anglers appeared to be individualistic in their choice of method. Overall, our study suggests that the adoption of modern reporting methods, like email and smartphone apps, could benefit data collection efforts of angler tag-return studies. Fisheries scientists may wish to consider which reporting methods align with their specific study objectives and with the angling public of a given study area.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/nafm.10738","usgsCitation":"Taylor, A., Pepper, A., Chapagain, B., Joshi, O., and Long, J.M., 2022, Modern reporting methods for angler tag-return studies:Trends in data quality, choice of method, and future considerations: North American Journal of Fisheries Management, v. 42, no. 1, p. 189-199, https://doi.org/10.1002/nafm.10738.","productDescription":"11 p.","startPage":"189","endPage":"199","ipdsId":"IP-131455","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433415,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Oklahoma","otherGeospatial":"Baron Fork, Caney Creek, Illinois River,","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -95,\n 36.333\n ],\n [\n -95,\n 35.666\n ],\n [\n -94.5,\n 35.666\n ],\n [\n -94.5,\n 36.333\n ],\n [\n -95,\n 36.333\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"42","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-12-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Taylor, A.T.","contributorId":286995,"corporation":false,"usgs":false,"family":"Taylor","given":"A.T.","email":"","affiliations":[{"id":54572,"text":"University of Central Oklahoma","active":true,"usgs":false}],"preferred":false,"id":908776,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pepper, A.M.","contributorId":341695,"corporation":false,"usgs":false,"family":"Pepper","given":"A.M.","email":"","affiliations":[{"id":54572,"text":"University of Central Oklahoma","active":true,"usgs":false}],"preferred":false,"id":908777,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chapagain, B.","contributorId":280237,"corporation":false,"usgs":false,"family":"Chapagain","given":"B.","email":"","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":908778,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Joshi, O.","contributorId":280236,"corporation":false,"usgs":false,"family":"Joshi","given":"O.","email":"","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":908779,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Long, James M. 0000-0002-8658-9949 jmlong@usgs.gov","orcid":"https://orcid.org/0000-0002-8658-9949","contributorId":3453,"corporation":false,"usgs":true,"family":"Long","given":"James","email":"jmlong@usgs.gov","middleInitial":"M.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908780,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70255290,"text":"70255290 - 2022 - Identifying translocation sites for a climate relict population of Finescale Dace","interactions":[],"lastModifiedDate":"2024-06-17T13:59:50.755342","indexId":"70255290","displayToPublicDate":"2021-12-11T08:52:14","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}},"title":"Identifying translocation sites for a climate relict population of Finescale Dace","docAbstract":"Translocation is a management strategy that seeks to address threats to fish and wildlife populations by establishing new populations in ecologically suitable areas. Populations of Finescale Dace Chrosomus neogaeus in the Great Plains may benefit from translocation, as they exhibit a climate relict natural history that has led to a disjunct distribution and minimal dispersal opportunities. We assessed the translocation suitability of sites for Finescale Dace in the Belle Fourche River basin, Wyoming–South Dakota, using a ranking approach for output from multiple analyses. We used multivariate analysis to evaluate dissimilarity in fish occurrence and habitat between sites with and without Finescale Dace in contemporary surveys (2018–2019; n = 68). We further evaluated the capacity for sites to support Finescale Dace under base case and future climate change scenarios using the predicted probability of occurrence (P) from species distribution models (SDMs) fitted with basinwide fish occurrence data from surveys conducted in 2008–2019 (n = 124) and spatially continuous environmental variables, including forecasted stream temperature scenarios. Sites with Finescale Dace tended to occur close to standing waterbodies, contained emergent vegetation cover, and did not exhibit large overlap in species-space with either native or nonnative species. Predicted P of Finescale Dace exhibited nonlinear relationships with mean August stream temperature, channel slope, and base flow index. The amount of suitable habitat (i.e., high predicted P) declined with forecasted stream warming scenarios in the SDMs. This study highlights the utility of using field observations, historical data, and forecasted climate change scenarios to assess translocation site suitability and inform management of at-risk native fish populations, and the results may be transferable to other populations with limited data or restricted distributions.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10348","usgsCitation":"Booher, E.C., and Walters, A.W., 2022, Identifying translocation sites for a climate relict population of Finescale Dace: Transactions of the American Fisheries Society, v. 151, no. 2, p. 245-259, https://doi.org/10.1002/tafs.10348.","productDescription":"15 p.","startPage":"245","endPage":"259","ipdsId":"IP-130982","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":430271,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South Dakota, Wyoming","otherGeospatial":"Belle Fourche River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -104.52520825238925,\n 44.94329248147119\n ],\n [\n -104.52520825238925,\n 44.52690501428299\n ],\n [\n -103.23360960816484,\n 44.52690501428299\n ],\n [\n -103.23360960816484,\n 44.94329248147119\n ],\n [\n -104.52520825238925,\n 44.94329248147119\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"151","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-02-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Booher, Evan C.J.","contributorId":339350,"corporation":false,"usgs":false,"family":"Booher","given":"Evan","email":"","middleInitial":"C.J.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":904105,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walters, Annika W. 0000-0002-8638-6682 awalters@usgs.gov","orcid":"https://orcid.org/0000-0002-8638-6682","contributorId":4190,"corporation":false,"usgs":true,"family":"Walters","given":"Annika","email":"awalters@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":904104,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70227751,"text":"70227751 - 2022 - The MODFLOW Application Programming Interface for simulationcontrol and software interoperability","interactions":[],"lastModifiedDate":"2022-01-28T14:36:36.187512","indexId":"70227751","displayToPublicDate":"2021-12-10T08:34:15","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7164,"text":"Environmental Modelling & Software","active":true,"publicationSubtype":{"id":10}},"title":"The MODFLOW Application Programming Interface for simulationcontrol and software interoperability","docAbstract":"The MODFLOW API allows other programs to control MODFLOW and interactively change variables without having to modify the source code. The MODFLOW API is based on the Basic Model Interface (BMI), which is a set of conventions that define how to initialize a simulation, update the model state by advancing in time, and finalize the run. For many existing MODFLOW coupling applications, the information provided to MODFLOW must be updated multiple times in a time step. As this capability to modify variables within a time step is not defined by the BMI, an extension to BMI was developed. This eXtended Model Interface is part of the MODFLOW API and allows such a tight coupling to other models. Examples are included for a variety of use cases, including new flexibility for users to develop custom packages without modifying the MODFLOW source code and coupling MODFLOW with other models and optimization libraries.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.envsoft.2021.105257","usgsCitation":"Hughes, J.D., Russcher, M.J., Langevin, C.D., Morway, E.D., and McDonald, R.R., 2022, The MODFLOW Application Programming Interface for simulationcontrol and software interoperability: Environmental Modelling & Software, v. 148, 105257, 14 p., https://doi.org/10.1016/j.envsoft.2021.105257.","productDescription":"105257, 14 p.","ipdsId":"IP-130102","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":449429,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.envsoft.2021.105257","text":"Publisher Index Page"},{"id":395044,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"148","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hughes, Joseph D. 0000-0003-1311-2354 jdhughes@usgs.gov","orcid":"https://orcid.org/0000-0003-1311-2354","contributorId":2492,"corporation":false,"usgs":true,"family":"Hughes","given":"Joseph","email":"jdhughes@usgs.gov","middleInitial":"D.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":832038,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Russcher, Martijn J. 0000-0001-8799-6514","orcid":"https://orcid.org/0000-0001-8799-6514","contributorId":272524,"corporation":false,"usgs":false,"family":"Russcher","given":"Martijn","email":"","middleInitial":"J.","affiliations":[{"id":36257,"text":"Deltares","active":true,"usgs":false}],"preferred":false,"id":832039,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Langevin, Christian D. 0000-0001-5610-9759 langevin@usgs.gov","orcid":"https://orcid.org/0000-0001-5610-9759","contributorId":1030,"corporation":false,"usgs":true,"family":"Langevin","given":"Christian","email":"langevin@usgs.gov","middleInitial":"D.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":832040,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Morway, Eric D. 0000-0002-8553-6140 emorway@usgs.gov","orcid":"https://orcid.org/0000-0002-8553-6140","contributorId":4320,"corporation":false,"usgs":true,"family":"Morway","given":"Eric","email":"emorway@usgs.gov","middleInitial":"D.","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":832041,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McDonald, Richard R. 0000-0002-0703-0638 rmcd@usgs.gov","orcid":"https://orcid.org/0000-0002-0703-0638","contributorId":2428,"corporation":false,"usgs":true,"family":"McDonald","given":"Richard","email":"rmcd@usgs.gov","middleInitial":"R.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":832042,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70231679,"text":"70231679 - 2022 - Ergodic site response model for subduction zone regions","interactions":[],"lastModifiedDate":"2022-05-20T11:34:16.226954","indexId":"70231679","displayToPublicDate":"2021-12-10T06:32:38","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1436,"text":"Earthquake Spectra","active":true,"publicationSubtype":{"id":10}},"title":"Ergodic site response model for subduction zone regions","docAbstract":"We present an ergodic site response model with regional adjustments for use with subduction zone ground-motion models. The model predicts site amplification of peak ground acceleration, peak ground velocity, and 5% damped pseudo-spectral accelerations of the orientation-independent horizonal component for oscillator periods from 0.01 to 10 s. The model depends on the time-averaged shear-wave velocity in the upper 30 m (VS30), basin depth, and region and is independent of subduction earthquake type. It has three components: a linear site-amplification term in the form of VS30-scaling, a nonlinear term that depends on VS30 and shaking intensity parameterized by peak ground acceleration at the reference-rock velocity condition of 760 m/s, and a basin sediment-depth term for Japan and Cascadia conditioned on the depth to the 2.5 km/s shear-wave velocity isosurface (Z2.5). A global VS30-scaling model is provided along with regional adjustments for Japan, Taiwan, South America, Alaska, and Cascadia. The nonlinear model is global, with a functional form that has often been used to fit nonlinear responses inferred from simulations, but here we calibrate it empirically. Relative to a prior model for shallow earthquakes in active tectonic regions, our subduction zone global VS30-scaling is comparable at short periods (<1.0 s) but weaker at long periods, while the nonlinear site response is generally less pronounced but extends to lower levels of shaking. Basin depth models are conditioned on the difference of the actual Z2.5 and a VS30-conditioned mean Z2.5. Sites with positive differential depths have increased long-period site responses and decreased short-period responses, with the opposite occurring for negative differential depths.
Palaeoceanographic changes can act as drivers of diversification and speciation, even in highly mobile marine organisms. Shearwaters are a group of globally distributed and highly mobile pelagic seabirds. Despite a recent well-resolved phylogeny, shearwaters have controversial species limits, and show periods of both slow and rapid diversification. Here, we explore the role of palaeoceanographic changes on shearwaters' diversification and speciation. We investigate shearwater biogeography and the evolution of a key phenotypic trait, body size, and we assess the validity of their current taxonomy.
Worldwide.
Shearwaters (Order Procellariiformes, Family Procellariidae, Genera Ardenna, Calonectris and Puffinus).
We generated genomic (ddRAD) data to infer a time-calibrated species tree for the shearwaters. We estimated ancestral ranges and evaluated the roles of founder events, vicariance and surface ocean currents in driving diversification. We performed phylogenetic generalised least squares to identify potential predictors of variability in body size along the phylogeny. To assess the validity of the current taxonomy, we analysed genomic patterns of recent shared ancestry and differentiation among shearwater taxa.
We identified a period of high dispersal and rapid speciation during the Late Pliocene–early Pleistocene. Species dispersal appears to be favoured by surface ocean currents, and founder events are supported as the main mode of speciation in these highly mobile pelagic seabirds. Body mass shows significant associations with life strategies and local conditions. The current taxonomy shows some incongruences with the patterns of genomic divergence.
A reduction of neritic areas during the Pliocene seems to have driven global extinctions of shearwater species, followed by a subsequent burst of speciation and dispersal probably promoted by Plio-Pleistocene climatic shifts. Our findings extend our understanding on the drivers of speciation and dispersal of highly mobile pelagic seabirds and shed new light on the important role of palaeoceanographic events.
","language":"English","publisher":"Wiley","doi":"10.1111/jbi.14291","usgsCitation":"Ferrer-Obiol, J., James, H.F., Chesser, R., Bretagnolle, V., Gonzalez-Solis, J., Rozas, J., Welch, A., and Riutort, M., 2022, Palaeoceanographic changes in the late Pliocene promoted rapid diversification in pelagic seabirds: Journal of Biogeography, v. 49, no. 1, p. 171-188, https://doi.org/10.1111/jbi.14291.","productDescription":"18 p.","startPage":"171","endPage":"188","ipdsId":"IP-123733","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":449436,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/jbi.14291","text":"Publisher Index Page"},{"id":396105,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"49","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-12-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Ferrer-Obiol, Joan","contributorId":279594,"corporation":false,"usgs":false,"family":"Ferrer-Obiol","given":"Joan","email":"","affiliations":[{"id":50463,"text":"Univ. of Barcelona","active":true,"usgs":false}],"preferred":false,"id":835165,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"James, Helen F.","contributorId":54414,"corporation":false,"usgs":false,"family":"James","given":"Helen","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":835166,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chesser, R. Terry 0000-0003-4389-7092","orcid":"https://orcid.org/0000-0003-4389-7092","contributorId":87669,"corporation":false,"usgs":true,"family":"Chesser","given":"R. Terry","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":835167,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bretagnolle, Vincent","contributorId":213757,"corporation":false,"usgs":false,"family":"Bretagnolle","given":"Vincent","email":"","affiliations":[{"id":38848,"text":"CNRS & Université de La Rochelle","active":true,"usgs":false}],"preferred":false,"id":835168,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gonzalez-Solis, Jacob 0000-0002-8691-9397","orcid":"https://orcid.org/0000-0002-8691-9397","contributorId":252896,"corporation":false,"usgs":false,"family":"Gonzalez-Solis","given":"Jacob","email":"","affiliations":[{"id":50463,"text":"Univ. of Barcelona","active":true,"usgs":false}],"preferred":false,"id":835169,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Rozas, Julio","contributorId":252897,"corporation":false,"usgs":false,"family":"Rozas","given":"Julio","email":"","affiliations":[{"id":50463,"text":"Univ. of Barcelona","active":true,"usgs":false}],"preferred":false,"id":835170,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Welch, Andreanna J.","contributorId":79313,"corporation":false,"usgs":false,"family":"Welch","given":"Andreanna J.","affiliations":[],"preferred":false,"id":835171,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Riutort, Marta","contributorId":252898,"corporation":false,"usgs":false,"family":"Riutort","given":"Marta","email":"","affiliations":[{"id":50463,"text":"Univ. of Barcelona","active":true,"usgs":false}],"preferred":false,"id":835172,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70227285,"text":"70227285 - 2022 - Towards a holistic sulfate-water-O2 triple oxygen isotope systematics","interactions":[],"lastModifiedDate":"2022-01-07T14:45:52.074811","indexId":"70227285","displayToPublicDate":"2021-12-08T08:44:08","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1213,"text":"Chemical Geology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Towards a holistic sulfate-water-O2 triple oxygen isotope systematics","title":"Towards a holistic sulfate-water-O2 triple oxygen isotope systematics","docAbstract":"Triple oxygen isotope (∆17O with δ18O) signals of H2O and O2 found in sulfate of oxidative weathering origin offer promising constraints on modern and ancient weathering, hydrology, atmospheric gas concentrations, and bioproductivity. However, interpretations of the sulfate-water-O2 system rely on assuming fixed oxygen-isotope fractionations between sulfate and water, which, contrastingly, are shown to vary widely in sign and amplitude. Instead, here we anchor sulfate-water-O2 triple oxygen isotope systematics on the homogeneous composition of atmospheric O2 with empirical constraints and modeling. Our resulting framework does not require a priori assumptions of the O2- versus H2O‑oxygen ratio in sulfate and accounts for the signals of mass-dependent and mass-independent fractionation in the ∆17O and δ18O of sulfate's O2‑oxygen source. Within this framework, new ∆17O measurements of sulfate constrain ~2.3 Ga Paleoproterozoic gross primary productivity to between 6 and 160 times present-day levels, with important implications for the biological carbon cycle response to high CO2 concentrations prevalent on the early Earth.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.chemgeo.2021.120678","usgsCitation":"Killingsworth, B.A., Cartigny, P., Hayles, J.A., Thomazo, C., Sansjofre, P., Pasquier, V., Lalonde, S.V., and Philippot, P., 2022, Towards a holistic sulfate-water-O2 triple oxygen isotope systematics: Chemical Geology, v. 588, 120678, 13 p., https://doi.org/10.1016/j.chemgeo.2021.120678.","productDescription":"120678, 13 p.","ipdsId":"IP-130808","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":449440,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.chemgeo.2021.120678","text":"Publisher Index Page"},{"id":394018,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"588","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Killingsworth, Bryan Alan 0000-0001-6067-8604","orcid":"https://orcid.org/0000-0001-6067-8604","contributorId":270978,"corporation":false,"usgs":true,"family":"Killingsworth","given":"Bryan","email":"","middleInitial":"Alan","affiliations":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"preferred":true,"id":830272,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cartigny, Pierre","contributorId":270979,"corporation":false,"usgs":false,"family":"Cartigny","given":"Pierre","email":"","affiliations":[{"id":56238,"text":"Institut de Physique du Globe de Paris, Sorbonne-Paris Cité, UMR 7154, CNRS-Université Paris Diderot","active":true,"usgs":false}],"preferred":false,"id":830273,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hayles, Justin A.","contributorId":270977,"corporation":false,"usgs":false,"family":"Hayles","given":"Justin","email":"","middleInitial":"A.","affiliations":[{"id":56237,"text":"Jacobs-JETS, Astromaterials Research and Exploration Science, Johnson Space Center National Aeronautics and Space Administration","active":true,"usgs":false}],"preferred":false,"id":830274,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Thomazo, Christophe","contributorId":270980,"corporation":false,"usgs":false,"family":"Thomazo","given":"Christophe","email":"","affiliations":[{"id":56239,"text":"UMR CNRS/uB 6282 Laboratoire Biogéosciences, Université de Bourgogne Franche-Comté and Institut Universitaire de France","active":true,"usgs":false}],"preferred":false,"id":830275,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sansjofre, Pierre","contributorId":270981,"corporation":false,"usgs":false,"family":"Sansjofre","given":"Pierre","email":"","affiliations":[{"id":56240,"text":"CNRS-UMR6538 Laboratoire Géosciences Océan, European Institute for Marine Studies, Université de Bretagne Occidentale","active":true,"usgs":false}],"preferred":false,"id":830276,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pasquier, Virgil","contributorId":270982,"corporation":false,"usgs":false,"family":"Pasquier","given":"Virgil","email":"","affiliations":[{"id":56241,"text":"Department of Earth and Planetary Sciences, Weizmann Institute of Science","active":true,"usgs":false}],"preferred":false,"id":830277,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Lalonde, Stefan V.","contributorId":196839,"corporation":false,"usgs":false,"family":"Lalonde","given":"Stefan","email":"","middleInitial":"V.","affiliations":[],"preferred":false,"id":830278,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Philippot, Pascal","contributorId":270983,"corporation":false,"usgs":false,"family":"Philippot","given":"Pascal","email":"","affiliations":[{"id":56242,"text":"Géosciences Montpellier, CNRS-UMR 5243, Université de Montpellier and Institut de Physique du Globe de Paris, Sorbonne-Paris Cité, UMR 7154, CNRS-Université Paris Diderot","active":true,"usgs":false}],"preferred":false,"id":830279,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70228218,"text":"70228218 - 2022 - Empirically validated drought vulnerability mapping in the mixed conifer forests of the Sierra Nevada","interactions":[],"lastModifiedDate":"2022-03-17T16:51:31.185083","indexId":"70228218","displayToPublicDate":"2021-12-07T09:33:20","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Empirically validated drought vulnerability mapping in the mixed conifer forests of the Sierra Nevada","docAbstract":"Severe droughts are predicted to become more frequent in the future, and the consequences of such droughts on forests can be dramatic, resulting in massive tree mortality, rapid change in forest structure and composition, and substantially increased risk of catastrophic fire. Forest managers have tools at their disposal to try to mitigate these effects but are often faced with limited resources, forcing them to make choices about which parts of the landscape to target for treatment. Such planning can greatly benefit from landscape vulnerability assessments, but many existing vulnerability analyses are unvalidated and not grounded in robust empirical datasets. We combined robust sets of ground-based plot and remote sensing data, collected during the 2012–2016 California drought, to develop rigorously validated tools for assessing forest vulnerability to drought-related canopy tree mortality for the mixed conifer forests of the Sequoia and Kings Canyon national parks and potentially for mixed conifer forests in the Sierra Nevada as a whole. Validation was carried out using a large external dataset. The best models included normalized difference vegetation index (NDVI), elevation, and species identity. Models indicated that tree survival probability decreased with greenness (as measured by NDVI) and elevation, particularly if trees were growing slowly. Overall, models showed good calibration and validation, especially for Abies concolor, which comprise a large majority of the trees in many mixed conifer forests in the Sierra Nevada. Our models tended to overestimate mortality risk for Calocedrus decurrens and underestimate risk for pine species, in the latter case probably due to pine bark beetle outbreak dynamics. Validation results indicated dangers of overfitting, as well as showing that the inclusion of trees already under attack by bark beetles at the time of sampling can give false confidence in model strength, while also biasing predictions. These vulnerability tools should be useful to forest managers trying to assess which parts of their landscape were vulnerable during the 2012–2016 drought, and, with additional validation, may prove useful for ongoing assessments and predictions of future forest vulnerability.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2514","usgsCitation":"Das, A., Slaton, M.R., Mallory, J., Asner, G.P., Martin, R.E., and Hardwick, P., 2022, Empirically validated drought vulnerability mapping in the mixed conifer forests of the Sierra Nevada: Ecological Applications, v. 32, no. 2, e2514, 19 p., https://doi.org/10.1002/eap.2514.","productDescription":"e2514, 19 p.","ipdsId":"IP-131799","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":436030,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9P6JKJW","text":"USGS data release","linkHelpText":"Calibration and Validation Data and Model Coefficients for Mixed Conifer Vulnerability Project from Sequoia and Kings Canyon National Park 2015 to 2019"},{"id":395619,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sequoia and Kings Canyon National Parks, Sierra Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.33349609375,\n 35.55010533588552\n ],\n [\n -117.828369140625,\n 35.55010533588552\n ],\n [\n -117.828369140625,\n 37.339591851359174\n ],\n [\n -119.33349609375,\n 37.339591851359174\n ],\n [\n -119.33349609375,\n 35.55010533588552\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"32","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-01-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Das, Adrian 0000-0002-3937-2616 adas@usgs.gov","orcid":"https://orcid.org/0000-0002-3937-2616","contributorId":201236,"corporation":false,"usgs":true,"family":"Das","given":"Adrian","email":"adas@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":833458,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Slaton, Michele R","contributorId":274868,"corporation":false,"usgs":false,"family":"Slaton","given":"Michele","email":"","middleInitial":"R","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":833459,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mallory, Jeffrey","contributorId":274869,"corporation":false,"usgs":false,"family":"Mallory","given":"Jeffrey","email":"","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":833460,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Asner, Gregory P.","contributorId":25393,"corporation":false,"usgs":false,"family":"Asner","given":"Gregory","email":"","middleInitial":"P.","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":833461,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Martin, Roberta E.","contributorId":201234,"corporation":false,"usgs":false,"family":"Martin","given":"Roberta","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":833462,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hardwick, Paul","contributorId":261559,"corporation":false,"usgs":false,"family":"Hardwick","given":"Paul","email":"","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":833463,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70226860,"text":"70226860 - 2022 - Complex demographic responses to contrasting climate drivers lead to divergent population trends across the range of a threatened alpine plant","interactions":[],"lastModifiedDate":"2021-12-16T12:53:03.866205","indexId":"70226860","displayToPublicDate":"2021-12-07T06:51:54","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Complex demographic responses to contrasting climate drivers lead to divergent population trends across the range of a threatened alpine plant","docAbstract":"Alpine plants are likely to be particularly vulnerable to climate change because of their restricted distributions and sensitivity to rapid environmental shifts occurring in high-elevation ecosystems. The well-studied Haleakalā silversword (‘āhinahina, Argyroxiphium sandwicense subsp. macrocephalum) already exhibits substantial climate-associated population decline, and offers the opportunity to understand the ecological and demographic mechanisms that underlie ongoing and predicted range shifts. We use nearly four decades of demographic monitoring for this threatened Hawaiian species, in combination with other biological, ecological and climate data to explore demographic responses across its entire range. We construct and independently validate population models for two elevation zones representing the species’ lower trailing and higher stable regions. Differences in population growth rate (lambda) between trailing and stable regions were influenced most strongly by lower survival of juvenile and small adult size classes, as well as by lower recruitment and lower survival of seedlings and large adults in the trailing region. Furthermore, seed production appears to have decreased from the 1980’s to present in the trailing region, and is now significantly less than in the stable region. Lambda and several underlying vital rates were significantly associated with wetter dry season conditions in the lower trailing region, indicating water limitation. In the higher elevation stable region, in contrast, lambda and vital rates were associated with warmer air temperatures, indicating cold limitation. These contrasting demographic patterns and climate dependencies lead to a high probability of extinction over the next century in the lower region, where most plants occur, but zero probability of the same in the higher region, according to stochastic population projections. Drier future scenarios further increase the probability of extinction at low elevations. The combined results illustrate the complexity in the demographic response and future viability that can occur across the range of a single species.
Historically, anadromous steelhead Oncorhynchus mykiss and spring-run Chinook Salmon O. tshawytscha used high-elevation rivers in the Sierra Nevada of California but were extirpated in the 20th century by construction of impassable dams. Plans to reintroduce the fish by opening migratory passage across the dams and reservoirs can only succeed if upstream habitats have the capacity to support viable populations of each species. To estimate capacity in the Tuolumne and Merced rivers of the central Sierra Nevada, we used a high-resolution approach based on remote sensing and dynamic habitat modeling. Our results suggested that for both species in both systems, sediment grain sizes would support widespread spawning and the water temperatures, depths, and velocities would generate ample capacity for fry and juveniles. However, the unregulated Merced River was consistently too warm for adult Chinook Salmon to hold in the dry season prior to spawning, while the regulated Tuolumne River maintained a cooler, more stable thermal regime with a capacity for thousands of holding adults. In our high-resolution approach, we also discovered several specific physical controls on life history expression, including thermal constraints on the timing of spawning, hydraulic prompts for downstream migration of fry, divergence of the hydraulic niches of steelhead and Chinook Salmon, and a key but uncertain role for thermal tolerance in adult Chinook Salmon. Our results suggested that steelhead reintroduction could succeed in either system and Chinook Salmon could succeed in the Tuolumne River if passage strategies account for large numbers of migrant fry and juveniles driven downstream by winter storms and snowmelt. The Merced River appeared too warm for adult Chinook Salmon, which raises questions about the current limited understanding of thermal tolerance in holding adults. Our study shows how a high-resolution approach can provide valuable insights on specific limiting factors that must be addressed for reintroduction to succeed.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10334","usgsCitation":"Boughton, D.A., Harrison, L.R., John, S.N., Bond, R.M., Nicol, C.L., Legleiter, C.J., and Richardson, R.T., 2022, Capacity of two Sierra Nevada rivers for reintroduction of anadromous salmonids: Insights from a high-resolution view: Transactions of the American Fisheries Society, v. 151, no. 1, p. 13-41, https://doi.org/10.1002/tafs.10334.","productDescription":"29 p.","startPage":"13","endPage":"41","ipdsId":"IP-123624","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":467211,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/tafs.10334","text":"External Repository"},{"id":436031,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MUPT5X","text":"USGS data release","linkHelpText":"Topographic, temperature, and sediment grain size data used to evaluate potential habitat for anadromous salmonids on the upper Merced and Tuolumne Rivers in California"},{"id":395229,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sierra Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.67907714843751,\n 37.08585785263673\n ],\n [\n -119.16320800781249,\n 37.08585785263673\n ],\n [\n -119.16320800781249,\n 38.07404145941957\n ],\n [\n -121.67907714843751,\n 38.07404145941957\n ],\n [\n -121.67907714843751,\n 37.08585785263673\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"151","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-12-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Boughton, David A.","contributorId":172477,"corporation":false,"usgs":false,"family":"Boughton","given":"David","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":832337,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harrison, Lee R.","contributorId":174322,"corporation":false,"usgs":false,"family":"Harrison","given":"Lee","email":"","middleInitial":"R.","affiliations":[{"id":6710,"text":"University of California, Santa Barbara, CA","active":true,"usgs":false}],"preferred":false,"id":832338,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"John, Sara N.","contributorId":273050,"corporation":false,"usgs":false,"family":"John","given":"Sara","email":"","middleInitial":"N.","affiliations":[{"id":12520,"text":"NOAA National Marine Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":832339,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bond, Rosealea M. 0000-0003-0939-2007","orcid":"https://orcid.org/0000-0003-0939-2007","contributorId":272853,"corporation":false,"usgs":false,"family":"Bond","given":"Rosealea","email":"","middleInitial":"M.","affiliations":[{"id":56398,"text":"Institute of Marine Sciences, University of California Santa Cruz and National Marine Fisheries Service, Southwest Fisheries Science Center","active":true,"usgs":false}],"preferred":false,"id":832340,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nicol, Colin L.","contributorId":201719,"corporation":false,"usgs":false,"family":"Nicol","given":"Colin","email":"","middleInitial":"L.","affiliations":[{"id":12520,"text":"NOAA National Marine Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":832341,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":832342,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Richardson, Ryan T. 0000-0002-7864-8670","orcid":"https://orcid.org/0000-0002-7864-8670","contributorId":272854,"corporation":false,"usgs":false,"family":"Richardson","given":"Ryan","email":"","middleInitial":"T.","affiliations":[{"id":56400,"text":"River Design Group","active":true,"usgs":false}],"preferred":false,"id":832343,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70228221,"text":"70228221 - 2022 - Crowding, climate, and the case for social distancing among trees","interactions":[],"lastModifiedDate":"2022-03-17T16:54:09.157512","indexId":"70228221","displayToPublicDate":"2021-12-06T09:44:32","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Crowding, climate, and the case for social distancing among trees","docAbstract":"In an emerging era of megadisturbance, bolstering forest resilience to wildfire, insects, and drought has become a central objective in many western forests. Climate has received considerable attention as a driver of these disturbances, but few studies have examined the complexities of climate–vegetation–disturbance interactions. Current strategies for creating resilient forests often rely on retrospective approaches, seeking to impart resilience by restoring historical conditions to contemporary landscapes, but historical conditions are becoming increasingly unattainable amidst modern bioclimatic conditions. What becomes an appropriate benchmark for resilience when we have novel forests, rapidly changing climate, and unprecedented disturbance regimes? We combined two longitudinal datasets—each representing some of the most comprehensive spatially explicit, annual tree mortality data in existence—in a post-hoc factorial design to examine the nonlinear relationships between fire, climate, forest spatial structure, and bark beetles. We found that while prefire drought elevated mortality risk, advantageous local neighborhoods could offset these effects. Surprisingly, mortality risk (Pm) was higher in crowded local neighborhoods that burned in wet years (Pm = 42%) compared with sparse neighborhoods that burned during drought (Pm = 30%). Risk of beetle attack was also increased by drought, but lower conspecific crowding impeded the otherwise positive interaction between fire and beetle attack. Antecedent fire increased drought-related mortality over short timespans (<7 years) but reduced mortality over longer intervals. These results clarify interacting disturbance dynamics and provide a mechanistic underpinning for forest restoration strategies. Importantly, they demonstrate the potential for managed fire and silvicultural strategies to offset climate effects and bolster resilience to fire, beetles, and drought.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2507","usgsCitation":"Furniss, T.J., Das, A., van Mantgem, P., Stephenson, N.L., and Lutz, J.A., 2022, Crowding, climate, and the case for social distancing among trees: Ecological Applications, v. 32, no. 2, e2507, 14 p., https://doi.org/10.1002/eap.2507.","productDescription":"e2507, 14 p.","ipdsId":"IP-126098","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":436032,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92SJXAD","text":"USGS data release","linkHelpText":"Sequoia and Yosemite National Parks Mortality and Fire Data (1990-2019) for Competition-Fire-Drought Interaction Analysis"},{"id":395622,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sierra Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.8173828125,\n 35.77325759103725\n ],\n [\n -118.57543945312501,\n 37.4530574713902\n ],\n [\n -119.44335937499999,\n 38.31149091244452\n ],\n [\n -119.761962890625,\n 38.302869955150044\n ],\n [\n -119.94873046875,\n 38.14751758025121\n ],\n [\n -119.93225097656251,\n 37.51844023887861\n ],\n [\n -119.20166015625,\n 36.71687068791304\n ],\n [\n -118.59191894531251,\n 35.67068501330236\n ],\n [\n -118.1634521484375,\n 35.505400093441324\n ],\n [\n -117.740478515625,\n 35.61711648382185\n ],\n [\n -117.8173828125,\n 35.77325759103725\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"32","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-01-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Furniss, Tucker J.","contributorId":181754,"corporation":false,"usgs":false,"family":"Furniss","given":"Tucker","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":833464,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Das, Adrian 0000-0002-3937-2616 adas@usgs.gov","orcid":"https://orcid.org/0000-0002-3937-2616","contributorId":201236,"corporation":false,"usgs":true,"family":"Das","given":"Adrian","email":"adas@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":833465,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"van Mantgem, Phillip J. 0000-0002-3068-9422","orcid":"https://orcid.org/0000-0002-3068-9422","contributorId":204320,"corporation":false,"usgs":true,"family":"van Mantgem","given":"Phillip J.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":833466,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stephenson, Nathan L. 0000-0003-0208-7229 nstephenson@usgs.gov","orcid":"https://orcid.org/0000-0003-0208-7229","contributorId":2836,"corporation":false,"usgs":true,"family":"Stephenson","given":"Nathan","email":"nstephenson@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":833467,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lutz, James A.","contributorId":139178,"corporation":false,"usgs":false,"family":"Lutz","given":"James","email":"","middleInitial":"A.","affiliations":[{"id":12682,"text":"Utah State University, Logan, UT","active":true,"usgs":false}],"preferred":false,"id":833468,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70226883,"text":"70226883 - 2022 - Both real-time and long-term environmental data perform well in predicting shorebird distributions in managed habitat","interactions":[],"lastModifiedDate":"2022-06-01T15:07:22.372804","indexId":"70226883","displayToPublicDate":"2021-12-06T07:09:24","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Both real-time and long-term environmental data perform well in predicting shorebird distributions in managed habitat","docAbstract":"Highly mobile species, such as migratory birds, respond to seasonal and inter-annual variability in resource availability by moving to better habitats. Despite the recognized importance of resource thresholds, species distribution models typically rely on long-term average habitat conditions, mostly because large-extent, temporally-resolved, environmental data are difficult to obtain. Recent advances in remote sensing make it possible to incorporate more frequent measurements of changing landscapes; however, there is often a cost in terms of model building and processing and the added value of such efforts is unknown. Our study tests whether incorporating real-time environmental data increases the predictive ability of distribution models, relative to using long-term average data. We developed and compared distribution models for shorebirds in California's Central Valley based on high temporal resolution (every 16-days), and 17-year long-term average, surface water data. Using abundance-weighted boosted regression trees, we modeled monthly shorebird occurrence as a function of surface water availability, crop type, wetland type, road density, temperature, and bird data source. While modeling with both real-time and long-term average data provided good fit to withheld validation data (0.79 < AUC < 0.89 across taxa), there were small differences in model performance. The best models incorporated long-term average conditions and spatial pattern information for real-time flooding (e.g. perimeter-area ratio of real-time water bodies). There was not a substantial difference in the performance of real-time and long-term average data models within time periods when real-time surface water differed substantially from the long-term average (specifically during drought years 2013-2016) and in intermittently flooded months or locations. Spatial predictions resulting from the models differed most in the southern region of the study area where there is lower water availability, fewer birds, and lower sampling density. Prediction uncertainty in the southern region of the study area highlights the need for increased sampling in this area. Because both sets of data performed similarly, the choice of which data to use may depend on the management context. Real-time data may ultimately be best for guiding dynamic, adaptive conservation actions whereas models based on long-term averages may be more helpful for guiding permanent wetland protection and restoration.
The mechanical, physical, and frictional properties of incoming materials play an important role in subduction zone structure and slip behavior because these properties influence the strength of the accretionary wedge and megathrust plate boundary faults. Incoming sediment sections often show an increase in compressional wave speed (Vp) and a decrease in porosity with depth due to consolidation. These relations allow seismic-velocity models to be used to elucidate properties and conditions at depth. However, variations in these properties are controlled by lithology and composition as well as cementation and diagenesis. We present an analysis of shipboard measurements of Vp and porosity on incoming sediment cores from International Ocean Discovery Program (IODP) expeditions at the Hikurangi Margin, Nankai Trough, Aleutian Trench, Middle America Trench, and Sunda Trench. Porosity for these samples ranges from 5% to 85% and Vp ranges from 1.5 to 6 km/s. Vp-porosity relations developed by Erikson & Jarrad (1998), https://doi.org/10.1029/98JB02128 and Hoffman & Tobin (2004) https://10.2973/odp.proc.sr.190196.355.2004, with a critical porosity of ∼30%, can represent carbonate-poor (<50 wt% CaCO3), mainly hemipelagic, incoming sediment regardless of the margin. But these relations tend to underestimate porosity in incoming sediments with carbonate content greater than 50 wt%, which appear to have a critical porosity of between 45% and 50%. This discrepancy will lead to inaccuracy in estimates of fluid budget and overpressure in subduction zones. The velocity-porosity relation in carbonate sediments is non-unique due to the complexity that results from the greater susceptibility of carbonate rocks to diagenetic processes.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021GC010074","usgsCitation":"Jeppson, T.N., and Kitajima, H., 2022, Velocity-porosity relations in carbonate and siliciclastic subduction zone input materials: Geochemistry, Geophysics, Geosystems, v. 23, no. 1, e2021GC010074, 15 p., https://doi.org/10.1029/2021GC010074.","productDescription":"e2021GC010074, 15 p.","ipdsId":"IP-130458","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":489114,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021gc010074","text":"Publisher Index Page"},{"id":393842,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","issue":"1","noUsgsAuthors":false,"publicationDate":"2021-12-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Jeppson, Tamara Nicole 0000-0001-5526-5530","orcid":"https://orcid.org/0000-0001-5526-5530","contributorId":248768,"corporation":false,"usgs":true,"family":"Jeppson","given":"Tamara","email":"","middleInitial":"Nicole","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":830061,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kitajima, Hiroko","contributorId":270795,"corporation":false,"usgs":false,"family":"Kitajima","given":"Hiroko","email":"","affiliations":[{"id":6747,"text":"Texas A&M University","active":true,"usgs":false}],"preferred":false,"id":830062,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}