Coastal resources are increasingly affected by erosion, extreme weather events, sea level rise, tidal flooding, and other potential hazards related to climate change. These hazards have varying effects on coastal landscapes because of the compounding of geologic, oceanographic, ecologic, and socioeconomic factors that exist at a given location. An assessment framework is introduced in this report that synthesizes existing datasets that cover the variability of the landscape, and hazards that may act on the landscape, to evaluate the likelihood of coastal change along the U.S. coastline on a decadal scale. The pilot study that aided in the development of the framework was run in the northeastern United States (from Maine to Virginia) and consists of datasets derived from a variety of Federal, State, and local sources.
First, a decision-tree-based dataset was built that describes the resistance or integrity of the coastal landscape (called the fabric dataset for the purposes of this report) and includes land cover, elevation, slope, long-term (more than 50 years) shoreline change, dune height, and marsh stability data. A second database was generated from coastal hazards, which are divided into event hazards (for example, flooding, wave power, and probability of storm overwash) and persistent or perpetual hazards (for example, relative sea level rise rate, short-term [about 30-year] shoreline erosion rate, and storm recurrence interval). The fabric dataset was then merged with the coastal hazards databases, and a model training dataset made up of hundreds of polygons was generated from these combined data to support machine learning.
The pilot study resulted in location-specific, 10-meter-resolution data classified into five raster datasets that include intrinsic characteristics of the coast used to determine the resistance of the landscape to change, the persistent and event hazards that act on the coast, the machine learning output (coastal change likelihood) based on the cumulative effects of the fabric and hazards datasets, and an estimate of the hazard type (event or persistent) that is the most likely to influence coastal change. Final outcomes are intended to be used as a first-order planning tool to determine which areas of the coast are more likely to change in response to future potential coastal hazards and to examine elements and drivers that make change in a location more likely.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1169","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Pendleton, E.A., Lentz, E.E., Sterne, T.K., and Henderson, R.E., 2023, Development and application of a coastal change likelihood assessment for the northeast region, Maine to Virginia: U.S. Geological Survey Data Report 1169, 56 p., https://doi.org/10.3133/dr1169.","productDescription":"Report: viii, 56 p.; Data Release","numberOfPages":"56","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-141482","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":413447,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96A2Q5X","text":"USGS data release","linkHelpText":"Coastal change likelihood in the U.S. northeast region—Maine to Virginia"},{"id":413449,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1169/images/"},{"id":499552,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114426.htm","linkFileType":{"id":5,"text":"html"}},{"id":413448,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1169/dr1169.XML"},{"id":413446,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/dr1169/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"DR 1169"},{"id":413445,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1169/dr1169.pdf","text":"Report","size":"26.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DR 1169"},{"id":413444,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1169/coverthb2.jpg"}],"country":"United States","state":"Connecticut, Delaware, Maine, Maryland, Massachusetts, New Hampshire, New Jersey, New York, Rhode Island, Virginia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.69060371478722,\n 36.642989353191695\n ],\n [\n -75.63687331767586,\n 36.61725767144067\n ],\n [\n -73.59796884586575,\n 40.01648840470193\n ],\n [\n -70.80274185094413,\n 41.01068598755887\n ],\n [\n -69.57806852142825,\n 41.099580604299234\n ],\n [\n -69.90622192003855,\n 42.11383028198776\n ],\n [\n -70.57455282622757,\n 43.02711001288796\n ],\n [\n -67.01522918769722,\n 44.713652472389384\n ],\n [\n -67.51934985246183,\n 45.183064414796405\n ],\n [\n -71.15470019361379,\n 43.73883250370727\n ],\n [\n -71.3079883194934,\n 41.86654548530123\n ],\n [\n -73.98393265041173,\n 41.230559521628294\n ],\n [\n -76.3263141198747,\n 39.710722533017474\n ],\n [\n -77.25368632549163,\n 38.78457338969852\n ],\n [\n -76.72175177570705,\n 36.72418776523644\n ],\n [\n -75.97037108334082,\n 36.81887134243617\n ],\n [\n -75.94893824149692,\n 36.817996199366135\n ],\n [\n -76.69060371478722,\n 36.642989353191695\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Woods Hole Coastal and Marine Science Center
U.S. Geological Survey
384 Woods Hole Road
Quissett Campus
Woods Hole, MA 02543-1598
The sensitivity of wildland plants to temperature can be directly measured using experimental manipulations of temperature in situ. We show that soil surface temperature and plant density (per square meter) have a significant impact on the germination, growth, and phenology of Bromus tectorum L., cheatgrass, a short-statured invasive winter-annual grass, and assess a new experimental temperature manipulation method: the application of black and white gravel to warm and cool the soil surface.
We monitored height, seed production, and phenological responses of cheatgrass, seeded into colored gravel at low and high densities at two sites in the western USA: Boise, ID and Cheyenne, WY. Soil surface temperature and volumetric water content were measured to assess treatment effects on soil surface microclimate.
Black gravel increased mean temperatures of the surface soil by 1.6 and 2.6 °C compared to white gravel in Cheyenne and Boise, respectively, causing 21–24 more days with soil temperatures > 0 °C, earlier cheatgrass germination, and up to 2.8-fold increases in cheatgrass height. Higher seeding density of cheatgrass led to 1.4-fold taller plants on black gravel plots at both sites, but not white gravel at the Boise site, indicating a possible thermal benefit or reduction of water demand due to plant clustering in warmer treatments.
Manipulating soil-surface albedo altered the soil microclimate and thus growth and phenology of cheatgrass, whose life history and growth form confer a strong dependency on soil-surface conditions.
","language":"English","publisher":"Springer","doi":"10.1007/s11104-023-05929-4","usgsCitation":"Maxwell, T.M., Germino, M., Romero, S., Porensky, L., Blumenthal, D.M., Brown, C., and Adler, P.B., 2023, Experimental manipulation of soil-surface albedo alters phenology and growth of Bromus tectorum (cheatgrass): Plant and Soil, v. 487, p. 325-339, https://doi.org/10.1007/s11104-023-05929-4.","productDescription":"15 p.","startPage":"325","endPage":"339","ipdsId":"IP-141275","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":413664,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Wyoming","city":"Boise, Cheyenne","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.35917072312003,\n 43.73889472078221\n ],\n [\n -116.35917072312003,\n 43.464686153948634\n ],\n [\n -116.05823287588329,\n 43.464686153948634\n ],\n [\n -116.05823287588329,\n 43.73889472078221\n ],\n [\n -116.35917072312003,\n 43.73889472078221\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -104.67448200616478,\n 41.2486838873686\n ],\n [\n -104.94665093511199,\n 41.2486838873686\n ],\n [\n -104.94665093511199,\n 41.04211139242014\n ],\n [\n -104.67448200616478,\n 41.04211139242014\n ],\n [\n -104.67448200616478,\n 41.2486838873686\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"487","noUsgsAuthors":false,"publicationDate":"2023-02-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Maxwell, Toby M. 0000-0001-5171-0705","orcid":"https://orcid.org/0000-0001-5171-0705","contributorId":302845,"corporation":false,"usgs":false,"family":"Maxwell","given":"Toby","email":"","middleInitial":"M.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":865613,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Germino, Matthew J. 0000-0001-6326-7579","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":251901,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":865614,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Romero, Seth","contributorId":302846,"corporation":false,"usgs":false,"family":"Romero","given":"Seth","email":"","affiliations":[{"id":6758,"text":"USDA-ARS","active":true,"usgs":false}],"preferred":false,"id":865615,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Porensky, Lauren M.","contributorId":264925,"corporation":false,"usgs":false,"family":"Porensky","given":"Lauren M.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":865616,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Blumenthal, Dana M.","contributorId":203896,"corporation":false,"usgs":false,"family":"Blumenthal","given":"Dana","email":"","middleInitial":"M.","affiliations":[{"id":36745,"text":"USDA-ARS Rangeland Resources Research Unit","active":true,"usgs":false}],"preferred":false,"id":865617,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brown, Cynthia S.","contributorId":302847,"corporation":false,"usgs":false,"family":"Brown","given":"Cynthia S.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":865618,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Adler, Peter B.","contributorId":64789,"corporation":false,"usgs":false,"family":"Adler","given":"Peter","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":865619,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70254300,"text":"70254300 - 2023 - Years of magma intrusion primed Kīlauea Volcano (Hawai'i) for the 2018 eruption: Evidence from olivine diffusion chronometry and monitoring data","interactions":[],"lastModifiedDate":"2024-05-17T11:55:00.666804","indexId":"70254300","displayToPublicDate":"2023-02-28T06:52:29","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"Years of magma intrusion primed Kīlauea Volcano (Hawai'i) for the 2018 eruption: Evidence from olivine diffusion chronometry and monitoring data","docAbstract":"The mechanisms that led to the exceptionally large Kīlauea 2018 eruption are still poorly understood and actively debated. External processes such as rainfall events or flank sliding have been proposed to play a triggering role. Here, we present field, geophysical, and petrological observations to show that internal changes within the magmatic plumbing system most likely led to the eruption. Chemical zoning in olivine crystals records the intrusion of primitive magma that is concurrent with deep seismicity and inflation at the volcano’s summit. Magma replenishment and pressurization of the summit reservoirs already started around 2014 and accelerated towards the eruption. Kīlauea volcano was therefore primed to experience a shift in eruptive activity in 2018. This pressure increase associated with reservoir replenishment may have been sufficient to overcome a previously blocked conduit. These findings imply that precursory signs of years of protracted magma intrusion and pressurization of the system may be recognizable in the future, which could lead to improved hazards mitigation.
Established in 1935, the CRU program is a unique cooperative partnership among State Fish and Wildlife agencies, host universities, Wildlife Management Institute, U.S. Geological Survey, and the U.S. Fish and Wildlife Service. Designed to meet the scientific needs of natural resource management agencies and to produce trained wildlife management professionals, the program has grown from the original 9 wildlife-only units to a program that today includes 42 units located on university campuses in 40 States.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1505","programNote":"Cooperative Research Units Program","usgsCitation":"Irwin, E.R., Dennerline, D.E., Grand, J.B., and Mawdsley, J., 2023, Cooperative Fish and Wildlife Research Units program—2022 year in review: U.S. Geological Survey Circular 1505, 44 p., https://doi.org/10.3133/cir1505.","productDescription":"viii, 44 p.","numberOfPages":"44","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-145409","costCenters":[{"id":203,"text":"Cooperative Research Unit Atlanta","active":false,"usgs":true}],"links":[{"id":413351,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://pubs.usgs.gov/publication/cir1477","text":"Circular 1477","linkHelpText":"- Cooperative Fish and Wildlife Research Units Program—2020 Research Abstracts"},{"id":413310,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1505/coverthb.jpg"},{"id":413311,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1505/cir1505.pdf","text":"Report","size":"16.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"CIR 1505"},{"id":413349,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://geonarrative.usgs.gov/2022cruyearinreview/","text":"Geonarrative","linkHelpText":"- 2022 Cooperative Fish and Wildlife Research Units Program—2022 Year in Review"},{"id":413350,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://geonarrative.usgs.gov/2022-2015cruyearinreview/","text":"Geonarrative","linkHelpText":"- 2015-2022 Cooperative Fish and Wildlife Research Units Program—2015-2022 Year in Review"}],"contact":"Cooperative Fish and Wildlife Research Units Program
U.S. Geological Survey
12201 Sunrise Valley Drive, Mail Stop 303
Reston, VA 20192
The U.S. Army Corps of Engineers (USACE) is considering changes to the management of water surface elevation in four lakes in the Mahoning River Basin. These changes would affect the timing and amounts of water released to the Mahoning River and could affect the water quality of those releases. To provide information on possible water-quality effects from these operational changes, flow and water-quality models were constructed for Berlin Lake, Lake Milton, Michael J Kirwan Reservoir, Mosquito Creek Lake, Mosquito Creek, and the Mahoning River from the dams downstream to Lowellville, Ohio.
The models were calibrated for two calendar years each, with model years selected depending on the availability of water-quality data. Models were developed with CE-QUAL-W2 version 4.2 (Wells, S.A., 2020, CE-QUAL-W2—A two-dimensional, laterally averaged, hydrodynamic and water quality model [version 4.2]: Portland State University, variously paged), a two-dimensional, laterally averaged hydrodynamic and water-quality model. Modeled constituents included flow, velocity, ice cover, water temperature, total dissolved solids (TDS), sulfate, chloride, inorganic suspended sediment, nitrate, ammonia, total Kjeldahl nitrogen, orthophosphate, total phosphorus, dissolved and particulate organic matter, algae, and dissolved oxygen. Iron was included for the lake models, but not the river.
A whole-basin model, with the four lake models and river model, was used to run model scenarios to examine the effects of altered lake water surface elevations on flow and water quality in the lakes, the lake outflows, and the Mahoning River. The initial whole-basin model, with calendar year 2013 hydrology and measured or typical water quality, was designated as scenario 0. Mahoning River flows for calendar year 2013 were close to a 20-year median flow. Four additional scenarios were constructed based on reservoir operations model (RES-SIM) model water surface elevations for the four lakes as provided by USACE. Scenario 1 was the RES-SIM base case, scenario 2 kept Berlin Lake water surface elevations higher in summer, scenario 3 allowed 25 percent of summer flood storage to extend the guide curve, and scenario 4 allowed more flexibility in lake management by removing any downstream Mahoning River minimum flow requirements. The Mahoning River model was not changed in any scenarios but received altered flows from the lakes. Significant findings from this study include the following:
Director, Oregon Water Science Center
U.S. Geological Survey
601 SW 2nd Avenue, Suite 1950
Portland, OR 97204
Bigheaded carp Hypophthalmichthys spp. are invasive species native to Asia expanding in the Mississippi River Basin in North America. An understanding of spatiotemporal distribution and aggregation of invasive carp is key to establishing when and where to focus surveillance designed to monitor expansion, and to managing harvest programs designed to curb population densities. We applied a two-stage hurdle model to assess three aspects of bigheaded carp ecology: distribution, relative abundance, and aggregation. Stage 1 was a binary 0/1 model that represented fish presence (p), and stage 2 was a truncated count distribution that had no zeros and included counts ≥ 1 only (C). Estimates of p and C varied temporally and spatially, but not in harmony and sometimes in opposing directions, indicating temporal and spatial swings in fish distributions and aggregations. Intense fish aggregations in channels in spring shown by low p’s and high C’s, eventually scattered by summer and fall as shown by high p’s and low C’s. An alternative but complementary interpretation of our observations is that p indexes incidence of aggregations and C indexes size of aggregations. Partitioning catch into its zero and nonzero components provided insight into population ecology that can inform development of monitoring and management of harvesting programs targeted at lessening potential effects of the invasion.
","language":"English","publisher":"Invasives.net","doi":"10.3391/mbi.2023.14.2.12","usgsCitation":"Miranda, L.E., Tompkins, J., Dunn, C.G., Morris, J.L., and Combs, M.C., 2023, Patterns of zero and nonzero counts suggest spatiotemporal distributions, aggregation, and dispersion of invasive carp: Management of Biological Invasions, v. 14, no. 2, p. 363-377, https://doi.org/10.3391/mbi.2023.14.2.12.","productDescription":"15 p.","startPage":"363","endPage":"377","ipdsId":"IP-130231","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":444351,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3391/mbi.2023.14.2.12","text":"Publisher Index Page"},{"id":433157,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Georgia, Kentucky, Mississippi, North Carolina, Tennessee","otherGeospatial":"Cumberland River basin, Kentucky Lake, Lake Barkley, Tennessee River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -86.6579330031289,\n 36.91383372177731\n ],\n [\n -89.3365987950857,\n 36.48519895640891\n ],\n [\n -90.06194179414129,\n 35.00234638850921\n ],\n [\n -88.19060222341875,\n 34.1587395703324\n ],\n [\n -84.99319129439488,\n 34.42656666413427\n ],\n [\n -82.51455380820883,\n 35.573734179364564\n ],\n [\n -81.53907530047877,\n 36.20934135136643\n ],\n [\n -83.66465109105143,\n 36.726966972925695\n ],\n [\n -86.6579330031289,\n 36.91383372177731\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Miranda, Leandro E. 0000-0002-2138-7924 smiranda@usgs.gov","orcid":"https://orcid.org/0000-0002-2138-7924","contributorId":531,"corporation":false,"usgs":true,"family":"Miranda","given":"Leandro","email":"smiranda@usgs.gov","middleInitial":"E.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908266,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tompkins, J.","contributorId":341343,"corporation":false,"usgs":false,"family":"Tompkins","given":"J.","email":"","affiliations":[{"id":53972,"text":"Kentucky Department of Fish and Wildlife Resources","active":true,"usgs":false}],"preferred":false,"id":908268,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dunn, Corey Garland 0000-0002-7102-2165","orcid":"https://orcid.org/0000-0002-7102-2165","contributorId":288691,"corporation":false,"usgs":true,"family":"Dunn","given":"Corey","email":"","middleInitial":"Garland","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908269,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Morris, J. L.","contributorId":255439,"corporation":false,"usgs":false,"family":"Morris","given":"J.","email":"","middleInitial":"L.","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":908267,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Combs, Matthew C.","contributorId":343671,"corporation":false,"usgs":false,"family":"Combs","given":"Matthew","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":911638,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70240990,"text":"70240990 - 2023 - Insights into the metamorphic history and origin of flake graphite mineralization at the Graphite Creek graphite deposit, Seward Peninsula, Alaska, USA","interactions":[],"lastModifiedDate":"2023-05-12T14:54:16.678942","indexId":"70240990","displayToPublicDate":"2023-02-27T08:19:55","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2746,"text":"Mineralium Deposita","active":true,"publicationSubtype":{"id":10}},"title":"Insights into the metamorphic history and origin of flake graphite mineralization at the Graphite Creek graphite deposit, Seward Peninsula, Alaska, USA","docAbstract":"Graphite Creek is an unusual flake graphite deposit located on the Seward Peninsula, Alaska, USA. We present field observations, uranium-lead (U–Pb) monazite and titanite geochronology, carbon (C) and sulfur (S) stable isotope geochemistry, and graphite Raman spectroscopy data from this deposit that support a new model of flake graphite ore genesis in high-grade metamorphic environments. The Graphite Creek deposit is within the second sillimanite metamorphic zone of the Kigluaik Mountains gneiss dome. Flake graphite, hosted in sillimanite-gneiss and quartz-biotite paragneiss, occurs as disseminations and in sets of very high grade (up to 50 wt.% graphite), semi-massive to massive graphite lenses 0.2 to 1 m wide containing quartz, sillimanite, inclusions of garnet porphyroblasts, K-feldspar, and tourmaline. Restitic garnet, sillimanite, graphite, and biotite accumulations indicate a high degree of anatexis and melt loss. Strong yttrium depletion in monazite, high europium ratios (Eu/Eu*), and excursions of high strontium and thorium concentrations are consistent with biotite dehydration melting. Monazite and titanite U–Pb ages record peak metamorphism from ~ 97 to 92 million years ago (Ma) and a retrograde event at ~ 85 Ma. Raman spectroscopy confirms the presence of carbonaceous material and highly ordered, crystalline graphite. Graphite δ13CVPDB values of − 30 to − 12‰ and pyrrhotite δ34SVCDT values of − 14 to 10‰ are consistent with derivation from organic carbon and sulfur in sedimentary rocks, respectively. These data collectively suggest that formation of massive graphite lenses occurred approximately synchronously with high-temperature metamorphism and anatexis of a highly carbonaceous pelitic protolith. Melt extraction and fluid release associated with anatexis were likely crucial for concentrating graphite. High-temperature, graphitic migmatite sequences within high-strain shear zones may be favorable for the occurrence of high-grade flake graphite deposits.
","language":"English","publisher":"Springer","doi":"10.1007/s00126-023-01161-3","usgsCitation":"Case, G.N., Karl, S.M., Regan, S., Johnson, C.A., Ellison, E.T., Caine, J., Holm-Denoma, C., Pianowski, L., and Benowitz, J.A., 2023, Insights into the metamorphic history and origin of flake graphite mineralization at the Graphite Creek graphite deposit, Seward Peninsula, Alaska, USA: Mineralium Deposita, v. 58, p. 939-962, https://doi.org/10.1007/s00126-023-01161-3.","productDescription":"24 p.","startPage":"939","endPage":"962","ipdsId":"IP-135671","costCenters":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"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}],"links":[{"id":444354,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00126-023-01161-3","text":"Publisher Index Page"},{"id":435431,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9J50EKX","text":"USGS data release","linkHelpText":"Data for Uranium-Lead Geochronology, Carbon and Sulfur Stable Isotopes, and Raman Spectroscopy from Graphite Creek, Alaska"},{"id":413658,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Graphite Creek graphite deposit, Seward Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -166.1974510679629,\n 65.1\n ],\n [\n -166.1974510679629,\n 64.7052203056632\n ],\n [\n -164.43596050024277,\n 64.7052203056632\n ],\n [\n -164.43596050024277,\n 65.1\n ],\n [\n -166.1974510679629,\n 65.1\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"58","noUsgsAuthors":false,"publicationDate":"2023-02-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Case, George N.D. 0000-0001-9826-5661 gcase@usgs.gov","orcid":"https://orcid.org/0000-0001-9826-5661","contributorId":224941,"corporation":false,"usgs":true,"family":"Case","given":"George","email":"gcase@usgs.gov","middleInitial":"N.D.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":865622,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Karl, Susan M. 0000-0003-1559-7826 skarl@usgs.gov","orcid":"https://orcid.org/0000-0003-1559-7826","contributorId":502,"corporation":false,"usgs":true,"family":"Karl","given":"Susan","email":"skarl@usgs.gov","middleInitial":"M.","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true}],"preferred":true,"id":865623,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Regan, Sean P.","contributorId":219815,"corporation":false,"usgs":false,"family":"Regan","given":"Sean P.","affiliations":[{"id":13599,"text":"University of Alaska - Fairbanks","active":true,"usgs":false}],"preferred":false,"id":865624,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Craig A. 0000-0002-1334-2996 cjohnso@usgs.gov","orcid":"https://orcid.org/0000-0002-1334-2996","contributorId":909,"corporation":false,"usgs":true,"family":"Johnson","given":"Craig","email":"cjohnso@usgs.gov","middleInitial":"A.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal 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":865625,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ellison, Eric T 0000-0002-6761-1397","orcid":"https://orcid.org/0000-0002-6761-1397","contributorId":302853,"corporation":false,"usgs":false,"family":"Ellison","given":"Eric","email":"","middleInitial":"T","affiliations":[{"id":52978,"text":"Department of Geological Sciences, University of Colorado, Boulder","active":true,"usgs":false}],"preferred":false,"id":865626,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Caine, Jonathan Saul 0000-0002-7269-6989 jscaine@usgs.gov","orcid":"https://orcid.org/0000-0002-7269-6989","contributorId":199295,"corporation":false,"usgs":true,"family":"Caine","given":"Jonathan Saul","email":"jscaine@usgs.gov","affiliations":[],"preferred":true,"id":865627,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Holm-Denoma, Christopher S. 0000-0003-3229-5440","orcid":"https://orcid.org/0000-0003-3229-5440","contributorId":219763,"corporation":false,"usgs":true,"family":"Holm-Denoma","given":"Christopher S.","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":865628,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pianowski, Laura 0000-0002-5346-8251","orcid":"https://orcid.org/0000-0002-5346-8251","contributorId":218817,"corporation":false,"usgs":true,"family":"Pianowski","given":"Laura","email":"","affiliations":[],"preferred":true,"id":865629,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Benowitz, Jeff A. 0000-0003-2294-9172","orcid":"https://orcid.org/0000-0003-2294-9172","contributorId":229570,"corporation":false,"usgs":false,"family":"Benowitz","given":"Jeff","email":"","middleInitial":"A.","affiliations":[{"id":41671,"text":"Geophysical Institute and Geochronology Laboratory, University of Alaska–Fairbanks","active":true,"usgs":false}],"preferred":false,"id":865630,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70247443,"text":"70247443 - 2023 - Laboratory and field comparisons of TFM bar formulations used to treat small streams for larval sea lamprey","interactions":[],"lastModifiedDate":"2023-08-08T12:23:17.972689","indexId":"70247443","displayToPublicDate":"2023-02-27T07:19:33","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2655,"text":"Management of Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Laboratory and field comparisons of TFM bar formulations used to treat small streams for larval sea lamprey","docAbstract":"A solid formulation of the pesticide TFM (4-nitro-3-(trifluoromethyl)-phenol) was developed in the 1980s for application in small tributaries during treatments to control invasive sea lamprey (Petromyzon marinus Linnaeus, 1758). Several initial inert ingredients were discontinued and substituted, culminating with an interim formulation that unacceptably softens and rapidly decays in warm conditions. A new TFM bar formulation was developed to resolve poor thermal stability and it was registered with the U.S. Environmental Protection Agency and Health Canada Pesticide Management Regulatory Agency in 2020. Laboratory studies compared the thermostability and dissolution (i.e., TFM release) of the interim and new formulation of TFM bars that were held at 20 C or 45 C for 24 hours prior to evaluation. Field tests compared the dissolution of the interim and new formulation of TFM bars when applied in three small tributaries in Michigan. Laboratory tests show that the new formulation bars remain usable when held at 45 C for 24 hours; whereas, the interim formulation bars partially liquify and are not usable. Field tests indicate the new formulation bars have superior characteristics including a near consistent release of TFM for 1013 hours when applied in waters with a velocity of < 0.06 m/sec. A near consistent release of TFM was observed for a maximum of about 6 hours in one field application of the interim formulation bars. Water temperature and water velocity influenced both formulations; however, the greatest effects were observed with interim formulation bars where higher initial TFM concentrations were followed by precipitous TFM concentration decreases in tributaries with the highest water temperature or velocity. Field treatment applications will provide data for refining application parameters such as the number of bars required per unit discharge at various water temperatures and the acceptable water velocity range for applications.","language":"English","publisher":"Regional Euro-Asian Biological Invasions Centre","doi":"10.3391/mbi.2023.14.2.11","usgsCitation":"Luoma, J.A., Schueller, J., Schloesser, N., Johnson, T., and Kirkeeng, C., 2023, Laboratory and field comparisons of TFM bar formulations used to treat small streams for larval sea lamprey: Management of Biological Invasions, v. 14, no. 2, p. 347-362, https://doi.org/10.3391/mbi.2023.14.2.11.","productDescription":"16 p.","startPage":"347","endPage":"362","ipdsId":"IP-139416","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":444356,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://doi.org/10.3391/mbi.2023.14.2.11","text":"Publisher Index Page"},{"id":435432,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P910SHBL","text":"USGS data release","linkHelpText":"Data Release for Laboratory and field comparisons of TFM bar formulations used to treat small streams for larval sea lamprey"},{"id":419593,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -84.06,\n 44.18\n ],\n [\n -84.06,\n 44.13\n ],\n [\n -84.00,\n 44.13\n ],\n [\n -84.00,\n 44.18\n ],\n [\n -84.06,\n 44.18\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","issue":"2","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Luoma, James A. 0000-0003-3556-0190 jluoma@usgs.gov","orcid":"https://orcid.org/0000-0003-3556-0190","contributorId":4449,"corporation":false,"usgs":true,"family":"Luoma","given":"James","email":"jluoma@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":879656,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schueller, Justin R. 0000-0002-7102-3889","orcid":"https://orcid.org/0000-0002-7102-3889","contributorId":213527,"corporation":false,"usgs":true,"family":"Schueller","given":"Justin","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":879657,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schloesser, Nicholas 0000-0002-3815-5302","orcid":"https://orcid.org/0000-0002-3815-5302","contributorId":237025,"corporation":false,"usgs":true,"family":"Schloesser","given":"Nicholas","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":879658,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Todd 0000-0003-2152-8528","orcid":"https://orcid.org/0000-0003-2152-8528","contributorId":261519,"corporation":false,"usgs":true,"family":"Johnson","given":"Todd","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":879659,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kirkeeng, Courtney A. 0000-0002-7141-1216","orcid":"https://orcid.org/0000-0002-7141-1216","contributorId":237026,"corporation":false,"usgs":true,"family":"Kirkeeng","given":"Courtney","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":879660,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70246257,"text":"70246257 - 2023 - Estimates of volcanic mercury emissions from Redoubt Volcano, Augustine Volcano, and Mount Spurr eruption ash","interactions":[],"lastModifiedDate":"2023-06-28T11:51:11.831526","indexId":"70246257","displayToPublicDate":"2023-02-27T06:48:58","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7753,"text":"Frontiers in Earth Science","active":true,"publicationSubtype":{"id":10}},"title":"Estimates of volcanic mercury emissions from Redoubt Volcano, Augustine Volcano, and Mount Spurr eruption ash","docAbstract":"Ash is a potential sink of volcanically sourced atmospheric mercury (Hg), and the concentration of particle-bound Hg may provide constraints on Hg emissions during eruptions. We analyze Hg concentrations in 227 bulk ash samples from the Mount Spurr (1992), Redoubt Volcano (2009), and Augustine Volcano (2006) volcanic eruptions to investigate large-scale spatial, temporal, and volcanic-source trends. We find no significant difference in Hg concentrations in bulk ash by distance or discrete eruptive events at each volcano, suggesting that in-plume reactions converting gaseous Hg0 to adsorbed Hg2+ are happening on shorter timescales than considered in this study (minutes) and any additional in-plume controls are not discernable within intra-volcanic sample variability. However, we do find a significant difference in Hg concentration of ash among volcanic sources, which indicates that volcanoes may emit comparatively high or low quantities of Hg. We combine our Hg findings with total mass estimates of ashfall deposits to calculate minimum, first-order Hg emissions of 8.23 t Hg for Mount Spurr (1992), 1.25 t Hg for Redoubt Volcano (2009), and 0.16 t Hg for Augustine Volcano (2006). In particular, we find that Mount Spurr is a high Hg emitting volcano, and that its 1992 particulate Hg emissions likely contributed substantially to the global eruptive volcanic Hg budget for that year. Based on our findings, previous approaches that use long-term Hg/SO2 mass ratios to estimate eruptive total Hg under-account for Hg emitted in explosive events, and global volcanogenic Total Hg estimates need revisiting.
Quantifying how contaminants change across life cycles of species that undergo metamorphosis is critical to assessing organismal risk, particularly for consumers. Pond-breeding amphibians can dominate aquatic animal biomass as larvae and are terrestrial prey as juveniles and adults. Thus, amphibians can be vectors of mercury exposure in both aquatic and terrestrial food webs. However, it is still unclear how mercury concentrations are affected by exogenous (e.g., habitat or diet) vs endogenous factors (e.g., catabolism during hibernation) as amphibians undergo large diet shifts and periods of fasting during ontogeny. We measured total mercury (THg), methylmercury (MeHg), and isotopic compositions (δ 13C, δ15N) in boreal chorus frogs (Pseudacris maculata) across five life stages in two Colorado (USA) metapopulations. We found large differences in concentrations and percent MeHg (of THg) among life stages. Frog MeHg concentrations peaked during metamorphosis and hibernation coinciding with the most energetically demanding life cycle stages. Indeed, life history transitions involving periods of fasting coupled with high metabolic demands led to large increases in mercury concentrations. The endogenous processes of metamorphosis and hibernation resulted in MeHg bioamplification, thus decoupling it from the light isotopic proxies of diet and trophic position. These step changes are not often considered in conventional expectations of how MeHg concentrations within organisms are assessed.
Climate change and human–wildlife conflict are both pressing challenges for biodiversity conservation and human well-being in the Anthropocene. Climate change is a critical yet underappreciated amplifier of human–wildlife conflict, as it exacerbates resource scarcity, alters human and animal behaviours and distributions, and increases human–wildlife encounters. We synthesize evidence of climate-driven conflicts occurring among ten taxonomic orders, on six continents and in all five oceans. Such conflicts disrupt both subsistence livelihoods and industrial economies and may accelerate the rate at which human–wildlife conflict drives wildlife declines. We introduce a framework describing distinct environmental, ecological and sociopolitical pathways through which climate variability and change percolate via complex social–ecological systems to influence patterns and outcomes of human–wildlife interactions. Identifying these pathways allows for developing mitigation strategies and proactive policies to limit the impacts of human–wildlife conflict on biodiversity conservation and human well-being in a changing climate.
Groundwater uranium (U) concentrations have been measured above the U.S. EPA maximum contaminant level (30 μg/L) in many U.S. aquifers, including in areas not associated with anthropogenic contamination by milling or mining. In addition to carbonate, nitrate has been correlated to uranium groundwater concentrations in two major U.S. aquifers. However, to date, direct evidence that nitrate mobilizes naturally occurring U from aquifer sediments has not been presented. Here, we demonstrate that the influx of high-nitrate porewater through High Plains alluvial aquifer silt sediments bearing naturally occurring U(IV) can stimulate a nitrate-reducing microbial community capable of catalyzing the oxidation and mobilization of U into the porewater. Microbial reduction of nitrate yielded nitrite, a reactive intermediate, which was further demonstrated to abiotically mobilize U from the reduced alluvial aquifer sediments. These results indicate that microbial activity, specifically nitrate reduction to nitrite, is one mechanism driving U mobilization from aquifer sediments in addition to previously described bicarbonate-driven desorption from mineral surfaces, such as Fe(III) oxides.
Infectious diseases pose a significant threat to global health and biodiversity. Yet, predicting the spatiotemporal dynamics of wildlife epizootics remains challenging. Disease outbreaks result from complex nonlinear interactions among a large collection of variables that rarely adhere to the assumptions of parametric regression modeling. We adopted a nonparametric machine learning approach to model wildlife epizootics and population recovery, using the disease system of colonial black-tailed prairie dogs (BTPD, Cynomys ludovicianus) and sylvatic plague as an example. We synthesized colony data between 2001 and 2020 from eight USDA Forest Service National Grasslands across the range of BTPDs in central North America. We then modeled extinctions due to plague and colony recovery of BTPDs in relation to complex interactions among climate, topoedaphic variables, colony characteristics, and disease history. Extinctions due to plague occurred more frequently when BTPD colonies were spatially clustered, in closer proximity to colonies decimated by plague during the previous year, following cooler than average temperatures the previous summer, and when wetter winter/springs were preceded by drier summers/falls. Rigorous cross-validations and spatial predictions indicated that our final models predicted plague outbreaks and colony recovery in BTPD with high accuracy (e.g., AUC generally >0.80). Thus, these spatially explicit models can reliably predict the spatial and temporal dynamics of wildlife epizootics and subsequent population recovery in a highly complex host–pathogen system. Our models can be used to support strategic management planning (e.g., plague mitigation) to optimize benefits of this keystone species to associated wildlife communities and ecosystem functioning. This optimization can reduce conflicts among different landowners and resource managers, as well as economic losses to the ranching industry. More broadly, our big data–model integration approach provides a general framework for spatially explicit forecasting of disease-induced population fluctuations for use in natural resource management decision-making.
","language":"English","publisher":"Wiley","doi":"10.1002/eap.2827","usgsCitation":"Barrile, G., Augustine, D.J., Porensky, L., Duchardt, C., Shoemaker, K., Hartway, C., Derner, J.D., Hunter, E.A., and Davidson, A.D., 2023, A big data–model integration approach for predicting epizootics and population recovery in a keystone species: Ecological Applications, v. 33, no. 4, e2827, 23 p., https://doi.org/10.1002/eap.2827.","productDescription":"e2827, 23 p.","ipdsId":"IP-142779","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467118,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.2827","text":"Publisher Index Page"},{"id":465980,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, Kansas, Montana, Nebraska, New Mexico, North Dakota, Oklahoma, South Dakota, Texas, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.72151393816772,\n 49.06842429079816\n ],\n [\n -106.74407678276975,\n 44.935250564946244\n ],\n [\n -105.02441274593369,\n 40.78587530801761\n ],\n [\n -105.18708495048385,\n 35.23907808129579\n ],\n [\n -110.74352872625728,\n 31.51616164533567\n ],\n [\n -101.12343743446304,\n 28.915776651344572\n ],\n [\n -100.73547916940133,\n 38.76206177537206\n ],\n [\n -100.82267130820097,\n 44.32291601997803\n ],\n [\n -101.89899715272732,\n 47.61230649548153\n ],\n [\n -111.72151393816772,\n 49.06842429079816\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"33","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-03-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Barrile, Gabriel M.","contributorId":288734,"corporation":false,"usgs":false,"family":"Barrile","given":"Gabriel M.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":922769,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Augustine, David J.","contributorId":189957,"corporation":false,"usgs":false,"family":"Augustine","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":922770,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Porensky, Lauren M.","contributorId":264925,"corporation":false,"usgs":false,"family":"Porensky","given":"Lauren M.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":922771,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duchardt, Courtney J.","contributorId":347959,"corporation":false,"usgs":false,"family":"Duchardt","given":"Courtney J.","affiliations":[{"id":7062,"text":"University of Oklahoma","active":true,"usgs":false}],"preferred":false,"id":922772,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shoemaker, Kevin T.","contributorId":288541,"corporation":false,"usgs":false,"family":"Shoemaker","given":"Kevin T.","affiliations":[{"id":61793,"text":"University of Nevada – Reno","active":true,"usgs":false}],"preferred":false,"id":922773,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hartway, Cynthia R.","contributorId":347961,"corporation":false,"usgs":false,"family":"Hartway","given":"Cynthia R.","affiliations":[{"id":13272,"text":"Wildlife Conservation Society","active":true,"usgs":false}],"preferred":false,"id":922774,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Derner, Justin D.","contributorId":195928,"corporation":false,"usgs":false,"family":"Derner","given":"Justin","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":922775,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hunter, Elizabeth Ann 0000-0003-4710-167X","orcid":"https://orcid.org/0000-0003-4710-167X","contributorId":288535,"corporation":false,"usgs":true,"family":"Hunter","given":"Elizabeth","email":"","middleInitial":"Ann","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":922776,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Davidson, Ana D. 0000-0003-4701-5923","orcid":"https://orcid.org/0000-0003-4701-5923","contributorId":304176,"corporation":false,"usgs":false,"family":"Davidson","given":"Ana","email":"","middleInitial":"D.","affiliations":[{"id":65991,"text":"CNHP","active":true,"usgs":false}],"preferred":false,"id":922777,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70241408,"text":"70241408 - 2023 - Poleward amplification, seasonal rainfall and forest heterogeneity in the Miocene of the eastern USA","interactions":[],"lastModifiedDate":"2023-03-17T11:42:04.586294","indexId":"70241408","displayToPublicDate":"2023-02-26T06:39:53","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1844,"text":"Global and Planetary Change","active":true,"publicationSubtype":{"id":10}},"title":"Poleward amplification, seasonal rainfall and forest heterogeneity in the Miocene of the eastern USA","docAbstract":"Paleoclimate reconstructions can provide a window into the environmental conditions in Earth history when atmospheric carbon dioxide concentrations were higher than today. In the eastern USA, paleoclimate reconstructions are sparse, because terrestrial sedimentary deposits are rare. Despite this, the eastern USA has the largest population and population density in North America, and understanding the effects of current and future climate change is of vital importance. Here, we provide terrestrial paleoclimate reconstructions of the eastern USA from Miocene fossil floras. Additionally, we compare proxy paleoclimate reconstructions from the warmest period in the Miocene, the Miocene Climatic Optimum (MCO), to those of an MCO Earth System Model. Reconstructed Miocene temperatures and precipitation north of 35°N are higher than modern. In contrast, south of 35°N, temperatures and precipitation are similar to today, suggesting a poleward amplification effect in eastern North America. Reconstructed Miocene rainfall seasonality was predominantly higher than modern, regardless of latitude, indicating greater variability in intra-annual moisture transport. Reconstructed climates are almost uniformly in the temperate seasonal forest biome, but heterogeneity of specific forest types is evident. Reconstructed Miocene terrestrial temperatures from the eastern USA are lower than modeled temperatures and coeval Atlantic sea surface temperatures. However, reconstructed rainfall is consistent with modeled rainfall. Our results show that during the Miocene, climate was most different from modern in the northeastern states, and may suggest a drastic reduction in the meridional temperature gradient along the North American east coast compared to today.
Fish that exhibit high foraging activity or bold behavior can be particularly vulnerable to angling. If these traits are heritable, selection through harvest can drive phenotypic change, eventually rendering a target population less vulnerable to angling and consequently impacting the quality of the fishery. In this study, we used parental-based tags to investigate whether vulnerability to angling might be heritable in steelhead trout (Oncorhynchus mykiss) spawned at a hatchery in western Oregon, USA. We found modest evidence to support the hypothesis that vulnerability to angling is a heritable trait in steelhead. However, our data unexpectedly revealed that steelhead collected with in-river traps produced nearly twice as many adult offspring as steelhead collected by anglers. This difference in adult-to-adult production is explained in part through lower egg-to-fry survival of steelhead produced with angler-caught broodstock, possibly related to collection stress and greater time in captivity experienced by angler-caught broodstock. Our findings suggest that managers could improve broodstock fitness and program efficiencies by preferentially spawning fish collected with traps, and limiting use of broodstock collected by anglers. Additional research is needed to identify mechanisms contributing to higher juvenile mortality of steelhead produced with angler-caught broodstock.
Diet plasticity is a common behavior exhibited by piscivores to sustain predator biomass when preferred prey biomass is reduced. Invasive piscivore diet plasticity could complicate suppression success; thus, understanding invasive predator consumption is insightful to meeting conservation targets. Here, we determine if diet plasticity exists in an invasive apex piscivore and whether plasticity could influence native species recovery benchmarks and invasive species suppression goals. We compared diet and stable isotope signatures of invasive lake trout and native Yellowstone cutthroat trout (cutthroat trout) from Yellowstone Lake, Wyoming, U.S.A. as a function of no, low-, moderate-, and high-lake trout density states. Lake trout exhibited plasticity in relation to their density; consumption of cutthroat trout decreased 5-fold (diet proportion from 0.89 to 0.18) from low- to high-density state. During the high-density state, lake trout switched to amphipods, which were also consumed by cutthroat trout, resulting in high diet overlap (Schoener’s index value, D = 0.68) between the species. As suppression reduced lake trout densities (moderate-density state), more cutthroat trout were consumed (proportion of cutthroat trout = 0.42), and diet overlap was released between the species (D = 0.30). A shift in lake trout δ13C signatures from the high- to the moderate-density state also corroborated increased consumption of cutthroat trout and lake trout diet plasticity. Observed declines in lake trout are not commensurate with expected cutthroat trout recovery due to lake trout diet plasticity. The abundance of the native species in need of conservation may take longer to recover due to the diet plasticity of the invasive species. The changes observed in diet, diet overlap, and isotopes associated with predator suppression provides more insight into conservation and suppression dynamics than using predator and prey biomass alone. By understanding these dynamics, we can better prepare conservation programs for potential feedbacks caused by invasive species suppression.
To assess the status and trends of conditions of surface waters throughout Mississippi, the U.S. Geological Survey, in cooperation with the Mississippi Department of Environmental Quality (MDEQ), summarized concentrations and estimated loads, yields, trends, and spatial and temporal patterns of total nitrogen (TN) and total phosphorus (TP) at 20 stream sites in MDEQ’s ambient water-quality monitoring network and 2 stream sites in the U.S. Geological Survey’s National Water-Quality Assessment Project’s monitoring network.
Comparison of streamflow at the time of water-quality sample collection to flow-duration curves for each site showed that samples were relatively evenly spread over a wide range of flows, indicating that load estimations were representative of a wide range of flows. Relation of streamflow to concentrations of TN and TP varied among sites and land use. Sites with high agriculture land use in the drainage basin tended to have a positive correlation between streamflow and concentration, suggesting influence of event-driven nonpoint-source runoff. Sites near urban (developed) areas tended to have a negative correlation between streamflow and concentration, suggesting chronic point-source influences during low-flow conditions. Sites with high forest land use and lower agriculture and urban (developed) land use showed little to no association between streamflow and concentration.
Seasonal distributions of concentrations of TN and TP also corresponded closely with variations in land use. Sites near urban (developed) land had the highest concentrations in late summer and fall, sites with a high percentage of agricultural land had the highest concentrations in the spring, and sites that were primarily forested or with little developed land did not exhibit substantial changes in concentration across seasons.
Eight sites had statistical likelihoods for upward trends of TN loads, and seven sites had statistical likelihoods for downward trends. Trends in TN loads at six sites were considered “about as likely as not,” meaning that a site has an equal chance of having an upward or downward trend. Trend results of mean annual flow-normalized loads of TP for the period of analysis (2008–18) showed that 16 sites had upward trends, 3 sites had downward trends, and 2 sites were considered “about as likely as not.”
Results from our study were compared to results from existing regional models to assess accuracy of predictions at a local scale. Comparisons of yields predicted from 2012 regional-scale SPAtially Referenced Regressions on Watershed attributes (SPARROW) to results from this study showed the 2012 SPARROW-predicted estimates varied in consistency with results from this study. The 2012 SPARROW-prediction model underestimated TN yields, more often and by a slightly larger degree, more than it overestimated TN yields. The 2012 SPARROW-predicted model tended to underestimate yields at study sites with higher yields. All four sites in the predominantly agricultural area of northwest Mississippi, locally known as the Mississippi Delta, were underestimated by 2012 SPARROW. For TP, yield comparisons at sites with lower yields were consistent, yields at sites with midrange yields tended to be overestimated by SPARROW, and yields at sites with high yields tended to be underestimated by SPARROW. TP yields at four sites in the Mississippi Delta were underestimated by the 2012 SPARROW-predicted model.
Results of select sites from our study were also compared to other published load estimates from an earlier time period to evaluate possible trends. Comparison of TN yields at four sites and TP yields at three sites from the study-derived estimates to estimates made from data spanning 1993–2004 showed decreasing TN yields at all four sites and decreasing TP yields at two of three sites, with increasing yields of TP at the Yazoo River lower site. Also, a third comparison of the TN and TP yields of the Yazoo River lower site of this study to estimates made from data spanning 1996–97 showed decreasing TN yields but similar TP yields. This suggests that TN yields may have decreased over the last 20–30 years, but TP yields remain constant or are increasing.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235003","issn":"ISSN 2328-0328","collaboration":"Prepared in cooperation with the Mississippi Department of Environmental Quality","usgsCitation":"Hicks, M.B., Crain, A.S., and Segrest, N.G., 2023, Status and trends of total nitrogen and total phosphorus concentrations, loads, and yields in streams of Mississippi, water years 2008–18: U.S. Geological Survey Scientific Investigations Report 2023–5003, 77 p., https://doi.org/10.3133/sir20235003.","productDescription":"Report: x, 77 p.; Data Release; Dataset","numberOfPages":"92","onlineOnly":"Y","ipdsId":"IP-130707","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":413300,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VV2U70","text":"USGS data release","linkHelpText":"Datasets of streamflow, nutrient concentrations, loads 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\"}}]}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
Coupled paleomagnetic and geochronologic data derived from mafic dykes provide valuable records of continental movement. To reconstruct the Proterozoic paleogeographic history of Peninsular India, we report paleomagnetic directions and U-Pb zircon ages from twenty-nine mafic dykes in the Eastern Dharwar Craton near Hyderabad. Paleomagnetic analysis yielded clusters of directional data that correspond to dyke swarms at 2.37 Ga, 2.22 Ga, 2.08 Ga, 1.89–1.86 Ga, 1.79 Ga, and a previously undated dual polarity magnetization. We report new positive baked contact tests for the 2.08 Ga swarm and the 1.89–1.86 Ga swarm(s), and a new inverse baked contact test for the 2.08 Ga swarm. Our results promote the 2.08 Ga Dharwar Craton paleomagnetic pole (43.1° N, 184.5° E; A95 = 4.3°) to a reliability score of R = 7 and suggest a position for the Dharwar Craton at 1.79 Ga based on a virtual geomagnetic pole (VGP) at 33.0° N, 347.5° E (a95 = 16.9°, k = 221, N = 2). The new VGP for the Dharwar Craton provides support for the union of the Dharwar, Singhbhum, and Bastar Cratons in the Southern India Block by at least 1.79 Ga. Combined new and published northeast-southwest moderate-steep dual polarity directions from Dharwar Craton dykes define a new paleomagnetic pole at 20.6° N, 233.1° E (A95 = 9.2°, N = 18; R = 5). Two dykes from this group yielded 1.05–1.01 Ga 207Pb/206Pb zircon ages and this range is taken as the age of the new paleomagnetic pole. A comparison of the previously published poles with our new 1.05–1.01 Ga pole shows India shifting from equatorial to higher (southerly) latitudes from 1.08 Ga to 1.01 Ga as a component of Rodinia.
The objective of the work presented in this report is to develop equations that can be used to extend the base flow record at multiple partial-record streamgaging stations at Acadia National Park in eastern coastal Maine based on nearby continuous-record streamgaging stations. Daily mean streamflow values at U.S. Geological Survey continuous-record streamgaging station Otter Creek near Bar Harbor, Maine (station 01022840) had stronger correlations with instantaneous measurements during base flow conditions from 2006 to 2020 at 14 partial-record streamgaging stations at Acadia National Park than the other four continuous-record streamgaging stations tested for use as index stations. Index stations are continuous-record stations on hydrologically similar streams that have the potential to be used to extend the record at the partial-record station. Base flow is that part of streamflow that is sustained primarily by groundwater discharge. It is not attributable to direct precipitation or melting snow. Five of the partial-record stations had strong correlations with Otter Creek (correlation coefficient greater than 0.90) and relatively low root mean square errors (from 0.04 to 0.19). An additional four partial-record stations had fair correlations with Otter Creek (correlation coefficient from 0.79 to 0.9) and relatively low root mean square errors (from 0.05 to 0.19). For these 10 stations, maintenance of variance extension type 1 (MOVE.1) record extension equations computed in this report provide a reasonable method for extending the partial record, estimating summer monthly means and medians, and estimating daily mean streamflow values at these sites on days with no streamflow (discharge) measurements. Four of the partial-record stations have weak correlations (less than 0.78) or high root mean square error values (greater than 9) or both, indicating that record extension techniques are not appropriate for these partial-record stations using currently [2022] available data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225126","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Lombard, P.J., 2023, Estimating streamflow for base flow conditions at partial-record streamgaging stations at Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2022–5126, 13 p., https://doi.org/10.3133/sir20225126.","productDescription":"Report: vi, 13 p.; Data Release","numberOfPages":"13","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-143769","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":413317,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZP8XHG","text":"USGS data release","linkHelpText":"Data and code to support MOVE.1 regression equations for streamflow at partial-record streamgaging stations at Acadia National Park, Maine:"},{"id":413315,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5126/sir20225126.XML"},{"id":413312,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5126/coverthb.jpg"},{"id":413313,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5126/sir20225126.pdf","text":"Report","size":"1.33 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5126"},{"id":413316,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5126/images/"},{"id":413864,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20225126/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5126"},{"id":500481,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114380.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Maine","otherGeospatial":"Acadia National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -68.16726259768689,\n 44.32624433734378\n ],\n [\n -68.17739845831488,\n 44.36973371484888\n ],\n [\n -68.21954229987337,\n 44.38307924264697\n ],\n [\n -68.23981402112935,\n 44.41090441296549\n ],\n [\n -68.28035746364178,\n 44.41928748605292\n ],\n [\n -68.29422758871185,\n 44.406712425764226\n ],\n [\n -68.33050330043281,\n 44.35371506667144\n ],\n [\n -68.38971806515461,\n 44.35562227826236\n ],\n [\n -68.40305472387581,\n 44.31937464393428\n ],\n [\n -68.39025153150348,\n 44.27890346604781\n ],\n [\n -68.3390387620142,\n 44.21661505589944\n ],\n [\n -68.3112985118746,\n 44.217762064180675\n ],\n [\n -68.2883594588743,\n 44.24566571236582\n ],\n [\n -68.30329651664208,\n 44.285396004838105\n ],\n [\n -68.24514868461804,\n 44.281958867796675\n ],\n [\n -68.1982036459197,\n 44.29532438221068\n ],\n [\n -68.16726259768689,\n 44.32662596339111\n ],\n [\n -68.16726259768689,\n 44.32624433734378\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Land management priorities and decisions may result in population declines for non-target wildlife species. In the western United States, large-scale removal of conifer from sagebrush ecosystems (Artemisia spp.) is occurring to recover greater sage-grouse (Centrocercus urophasianus) populations and may result in pinyon jay (Gymnorhinus cyanocephalus) habitat loss. Jay populations have experienced long-term declines, due to unknown causes, resulting in a recent petition for listing under the Endangered Species Act of 1973. We developed a Bayesian hierarchical model of jay abundance, using 13 years of point count data (2008–2020) collected across the western United States, to estimate regional population trends, model habitat requirements, assess conifer removal effects on jays, and generate hypotheses regarding jay population declines. Our model included climate and landcover covariates and regional trends in pinyon jay density. We applied our modeled habitat relationships to map predicted pinyon jay density, given 2008 and 2020 resource conditions, and map density changes from 2008 to 2020. Our results indicate pinyon jay populations are declining within Bird Conservation Region 16. Jay density was positively associated with sagebrush cover, Palmer Drought Severity Index, and pinyon-juniper cover. Conversely, jay populations were negatively associated with Normalized Difference Vegetation Index (NDVI). We found higher pinyon jay densities within locations possessing both sagebrush and pinyon-juniper cover; conditions characteristic of phase I and II conifer encroachment which are preferentially targeted for conifer removal to restore sagebrush communities. Conifer removal, if conducted at locations with high pinyon jay densities, is therefore likely to negatively affect jay abundance.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2023.109959","usgsCitation":"Van Lanen, N.J., Monroe, A., and Aldridge, C.L., 2023, A hidden cost of single species management: Habitat-relationships reveal potential negative effects of conifer removal on a non-target species: Biological Conservation, v. 280, 109959, 10 p., https://doi.org/10.1016/j.biocon.2023.109959.","productDescription":"109959, 10 p.","ipdsId":"IP-138764","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":444374,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.biocon.2023.109959","text":"Publisher Index Page"},{"id":435435,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NIG4UW","text":"USGS data release","linkHelpText":"Predicted Pinyon Jay (Gymnorhinus cyanocephalus) densities across the western United States, 2008-2020"},{"id":413855,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Kansas, Montana, Nebraska, Nevada, North Dakota, South Dakota, Utah, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -115.41395617933225,\n 35.71066116858752\n ],\n [\n -111.67021215517931,\n 35.905346739347536\n ],\n [\n -108.77088674515161,\n 36.96389200169858\n ],\n [\n -101.86862285960521,\n 37.125401887525115\n ],\n [\n -101.51491491061196,\n 37.52404629916971\n ],\n [\n -101.90911987227537,\n 41.291485987900245\n ],\n [\n -103.26229497324951,\n 42.46761717574853\n ],\n [\n -101.97643578414241,\n 43.25420173811844\n ],\n [\n -102.55180019651098,\n 49.041860323717856\n ],\n [\n -117.14265326477982,\n 49.014048521848\n ],\n [\n -116.95141239209565,\n 46.12283190981066\n ],\n [\n -116.941510874859,\n 40.99815769875613\n ],\n [\n -120.0102098874165,\n 38.93737098892768\n ],\n [\n -120.02540765639762,\n 38.10974034171085\n ],\n [\n -115.41395617933225,\n 35.71066116858752\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"280","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Van Lanen, Nicholas J. 0000-0003-0871-0261","orcid":"https://orcid.org/0000-0003-0871-0261","contributorId":302927,"corporation":false,"usgs":true,"family":"Van Lanen","given":"Nicholas","email":"","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":865859,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Monroe, Adrian P. 0000-0003-0934-8225 amonroe@usgs.gov","orcid":"https://orcid.org/0000-0003-0934-8225","contributorId":152209,"corporation":false,"usgs":true,"family":"Monroe","given":"Adrian P.","email":"amonroe@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":865860,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":865861,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70241942,"text":"70241942 - 2023 - Changes in mangrove blue carbon under elevated atmospheric CO2","interactions":[],"lastModifiedDate":"2023-03-31T13:41:21.573249","indexId":"70241942","displayToPublicDate":"2023-02-23T08:38:08","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5075,"text":"Ecosystem Health and Sustainability","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Changes in mangrove blue carbon under elevated atmospheric CO2","title":"Changes in mangrove blue carbon under elevated atmospheric CO2","docAbstract":"While there is consensus that blue carbon ecosystems, such as mangroves, have an important role in mitigating some aspects of global climate change, little is known about mangrove carbon cycling under elevated atmospheric CO2 concentrations (eCO2). Here, we review studies in order to identify pathways for how eCO2 might influence mangrove ecosystem carbon cycling. In general, eCO2 alters plant productivity, species community composition, carbon fluxes, and carbon deposition in ways that enhance mangrove carbon storage with eCO2. As a result, a negative feedback to climate change exists whereby eCO2 adds to mangrove’s ability to sequester additional carbon, which in turn reduces the rate by which CO2 builds. Furthermore, eCO2 affects warming and sea-level rise (SLR) through alternate pathways, which coinfluence the mangrove response in both antagonistic (i.e., warming = greater carbon loss to decomposition) and synergistic (i.e., SLR = greater soil carbon burial) ways. eCO2 is projected to become a more prominent driver in the future before reaching a steady state. However, given the complexity of the interactions of biological and environmental factors with eCO2, long-term field observations and in situ simulation experiments can help to better understand the mechanisms for proper model initialization to predict future changes in mangrove carbon sequestration.
","language":"English","publisher":"American Association for the Advancement of Science","doi":"10.34133/ehs.0033","usgsCitation":"Gu, X., Qiao, P., Krauss, K., Lovelock, C.E., Adams, J.B., Chapman, S.K., Jennerjahn, T.C., Lin, Q., and Chen, L., 2023, Changes in mangrove blue carbon under elevated atmospheric CO2: Ecosystem Health and Sustainability, v. 9, 0033, 12 p., https://doi.org/10.34133/ehs.0033.","productDescription":"0033, 12 p.","ipdsId":"IP-146217","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":444376,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.34133/ehs.0033","text":"Publisher Index Page"},{"id":415007,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gu, Xiaoxuan","contributorId":296950,"corporation":false,"usgs":false,"family":"Gu","given":"Xiaoxuan","email":"","affiliations":[{"id":64251,"text":"College of the Environment and Ecology, Xiamen University","active":true,"usgs":false}],"preferred":false,"id":868298,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Qiao, Peiyang","contributorId":303861,"corporation":false,"usgs":false,"family":"Qiao","given":"Peiyang","email":"","affiliations":[{"id":47617,"text":"Xiamen University, China","active":true,"usgs":false}],"preferred":false,"id":868299,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Krauss, Ken 0000-0003-2195-0729","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":222378,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":868300,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lovelock, Catherine E.","contributorId":215562,"corporation":false,"usgs":false,"family":"Lovelock","given":"Catherine","email":"","middleInitial":"E.","affiliations":[{"id":39280,"text":"School of Biological Sciences, The University of Queensland","active":true,"usgs":false}],"preferred":false,"id":868301,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Adams, Janine B.","contributorId":303863,"corporation":false,"usgs":false,"family":"Adams","given":"Janine","email":"","middleInitial":"B.","affiliations":[{"id":65919,"text":"Nelson Mandela University (South Africa)","active":true,"usgs":false}],"preferred":false,"id":868302,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Chapman, Samantha K.","contributorId":303864,"corporation":false,"usgs":false,"family":"Chapman","given":"Samantha","email":"","middleInitial":"K.","affiliations":[{"id":12766,"text":"Villanova University","active":true,"usgs":false}],"preferred":false,"id":868303,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jennerjahn, Tim C.","contributorId":303865,"corporation":false,"usgs":false,"family":"Jennerjahn","given":"Tim","email":"","middleInitial":"C.","affiliations":[{"id":65921,"text":"Leibniz Centre for Tropical Marine Research (ZMT), Germany","active":true,"usgs":false}],"preferred":false,"id":868304,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Lin, Qiulian","contributorId":294476,"corporation":false,"usgs":false,"family":"Lin","given":"Qiulian","email":"","affiliations":[{"id":63579,"text":"Xiamen University","active":true,"usgs":false}],"preferred":false,"id":868305,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Chen, Luzhen","contributorId":194706,"corporation":false,"usgs":false,"family":"Chen","given":"Luzhen","email":"","affiliations":[],"preferred":false,"id":868306,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70241240,"text":"70241240 - 2023 - Combinatorial optimization of earthquake spatial distributions under minimum cumulative stress constraints","interactions":[],"lastModifiedDate":"2023-05-25T15:52:09.064418","indexId":"70241240","displayToPublicDate":"2023-02-23T08:30:12","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Combinatorial optimization of earthquake spatial distributions under minimum cumulative stress constraints","docAbstract":"We determine optimal on‐fault earthquake spatial distributions using a combinatorial method that minimizes the long‐term cumulative stress resolved on the fault. An integer‐programming framework was previously developed to determine the optimal arrangement of a millennia‐scale earthquake sample that minimizes the misfit to a target slip rate determined from geodetic data. The resulting cumulative stress from just slip‐rate optimization, however, can greatly exceed fault strength estimates. Therefore, we add an objective function that minimizes cumulative stress and broad stress constraints to limit the solution space. We find that there is a trade‐off in the two objectives: minimizing the cumulative stress on a fault within fault strength limits concentrates earthquakes in specific areas of the fault and results in excursions from the target slip rate. Both slip‐rate and stress objectives can be combined in either a weighted or lexicographic (hierarchical) method. Using a combination of objectives, we demonstrate that a Gutenberg–Richter sample of earthquakes can be arranged on a constant slip‐rate finite fault with minimal stress and slip‐rate residuals. We apply this method to determine the optimal arrangement of earthquakes on the variable slip‐rate Nankai megathrust over 5000 yr. The sharp decrease in slip rate at the Tokai section of the fault results in surplus cumulative stress under all scenarios. Using stress optimization alone restricts this stress surplus to the northeast end of the fault at the expense of decreasing the slip rate away from the target slip rate at the southwest end of the fault. A combination of both slip‐rate and stress objectives provides an adequate fit to the data, although alternate model formulations for the fault are needed at the Tokai section to explain persistent excess cumulative stress. In general, incorporating stress objectives and constraints into the integer‐programming framework adds an important aspect of fault physics to the resulting earthquake rupture forecasts.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120220175","usgsCitation":"Geist, E.L., and Parsons, T.E., 2023, Combinatorial optimization of earthquake spatial distributions under minimum cumulative stress constraints: Bulletin of the Seismological Society of America, v. 113, no. 3, p. 1025-1038, https://doi.org/10.1785/0120220175.","productDescription":"14 p.","startPage":"1025","endPage":"1038","ipdsId":"IP-144689","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":414280,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"113","issue":"3","noUsgsAuthors":false,"publicationDate":"2023-02-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Geist, Eric L. 0000-0003-0611-1150","orcid":"https://orcid.org/0000-0003-0611-1150","contributorId":15543,"corporation":false,"usgs":true,"family":"Geist","given":"Eric","email":"","middleInitial":"L.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":866627,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Parsons, Thomas E. 0000-0002-0582-4338 tparsons@usgs.gov","orcid":"https://orcid.org/0000-0002-0582-4338","contributorId":2314,"corporation":false,"usgs":true,"family":"Parsons","given":"Thomas","email":"tparsons@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":866628,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}