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.
Conserving genetic connectivity is fundamental to species persistence, yet rarely is made actionable into spatial planning for imperilled species. Climate change and habitat degradation have added urgency to embrace connectivity into networks of protected areas. Our two-step process integrates a network model with a functional connectivity model, to identify population centres important to maintaining genetic connectivity then to delineate those pathways most likely to facilitate connectivity thereamong for the greater sage-grouse (Centrocercus urophasianus), a species of conservation concern ranging across eleven western US states and into two Canadian provinces. This replicable process yielded spatial action maps, able to be prioritized by importance to maintaining range-wide genetic connectivity. We used these maps to investigate the efficacy of 3.2 million ha designated as priority areas for conservation (PACs) to encompass functional connectivity. We discovered that PACs encompassed 41.1% of cumulative functional connectivity—twice the amount of connectivity as random—and disproportionately encompassed the highest-connectivity landscapes. Comparing spatial action maps to impedances to connectivity such as cultivation and woodland expansion allows both planning for future management and tracking outcomes from past efforts.
","language":"English","publisher":"The Royal Society Publishing","doi":"10.1098/rsos.220437","usgsCitation":"Cross, T.B., Tack, J.D., Naugle, D., Schwartz, M.D., Doherty, K., Oyler-McCance, S.J., Pritchert, R.D., and Fedy, B.C., 2023, The ties that bind the sagebrush biome: Integrating genetic connectivity into range-wide conservation of greater sage-grouse: Royal Society Open Science, v. 10, no. 2, 220437, 15 p., https://doi.org/10.1098/rsos.220437.","productDescription":"220437, 15 p.","ipdsId":"IP-136470","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":444387,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rsos.220437","text":"Publisher Index Page"},{"id":435437,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HI7OGR","text":"USGS data release","linkHelpText":"Greater sage-grouse network-prioritized functional connectivity cumulative current map (raster)"},{"id":413667,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"western United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -103.07007462178439,\n 48.995456978554444\n ],\n [\n -119.8990141514297,\n 48.995456978554444\n ],\n [\n -119.8990141514297,\n 36.393670817249514\n ],\n [\n -103.07007462178439,\n 36.393670817249514\n ],\n [\n -103.07007462178439,\n 48.995456978554444\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"10","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-02-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Cross, Todd B.","contributorId":189267,"corporation":false,"usgs":false,"family":"Cross","given":"Todd","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":865592,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tack, Jason D. jtack@usgs.gov","contributorId":302682,"corporation":false,"usgs":false,"family":"Tack","given":"Jason","email":"jtack@usgs.gov","middleInitial":"D.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":865593,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Naugle, David E.","contributorId":255114,"corporation":false,"usgs":false,"family":"Naugle","given":"David E.","affiliations":[{"id":51432,"text":"W.A. Franke College of Forestry and Conservation, University of Montana, Missoula, MT, 59812, USA","active":true,"usgs":false}],"preferred":false,"id":865594,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Schwartz, Michael D.","contributorId":174566,"corporation":false,"usgs":false,"family":"Schwartz","given":"Michael","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":865595,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Doherty, Kevin E.","contributorId":177793,"corporation":false,"usgs":false,"family":"Doherty","given":"Kevin E.","affiliations":[],"preferred":false,"id":865596,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Oyler-McCance, Sara J. 0000-0003-1599-8769 sara_oyler-mccance@usgs.gov","orcid":"https://orcid.org/0000-0003-1599-8769","contributorId":1973,"corporation":false,"usgs":true,"family":"Oyler-McCance","given":"Sara","email":"sara_oyler-mccance@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":865597,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Pritchert, Ronald D.","contributorId":218059,"corporation":false,"usgs":false,"family":"Pritchert","given":"Ronald","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":865598,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Fedy, Brad C.","contributorId":140877,"corporation":false,"usgs":false,"family":"Fedy","given":"Brad","email":"","middleInitial":"C.","affiliations":[{"id":6655,"text":"University of Waterloo","active":true,"usgs":false}],"preferred":false,"id":865599,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70240217,"text":"ofr20221121 - 2023 - Observations of coastal circulation, waves, and sediment transport along West Maui, Hawaiʻi (November 2017– March 2018), and modeling effects of potential watershed restoration on decreasing sediment loads to adjacent coral reefs","interactions":[],"lastModifiedDate":"2023-02-23T11:58:55.644522","indexId":"ofr20221121","displayToPublicDate":"2023-02-22T09:06:04","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1121","displayTitle":"Observations of Coastal Circulation, Waves, and Sediment Transport Along West Maui, Hawaiʻi (November 2017– March 2018), and Modeling Effects of Potential Watershed Restoration on Decreasing Sediment Loads to Adjacent Coral Reefs","title":"Observations of coastal circulation, waves, and sediment transport along West Maui, Hawaiʻi (November 2017– March 2018), and modeling effects of potential watershed restoration on decreasing sediment loads to adjacent coral reefs","docAbstract":"Terrestrial sediment discharging from watersheds off West Maui, Hawaiʻi, has been documented as a primary stressor to local coral reefs, causing coral reef health to decline. The U.S. Geological Survey acquired and analyzed physical oceanographic and sedimentologic field data off the coast of West Maui to calibrate and validate physics-based, numerical hydrodynamic and sediment transport models of the study area developed by Deltares. These models simulated terrestrial sediment transport and dispersal from West Maui watersheds into coastal waters and how terrestrial sediment affects nearby coral reefs under different oceanographic forcing and watershed restoration scenarios.
Wave energy and near-bed turbidity are positively correlated in the field observations, illustrating a process not captured by the model simulations in which sediment already deposited on the seabed is resuspended by wave action and subsequently transported by prevailing currents. In the model simulations, large waves during flood events led to a decrease in suspended-sediment concentrations. Notably, however, the model results only consider sediment entering coastal waters from five stream sources and do not simulate sediment already present on the seabed.
The model simulations project that the Honokeana and Māhinahina coral reefs would experience the greatest reduction in sediment impacts from theoretical watershed restoration. Additionally, when large waves coincide with flood events, post-storm sedimentation generally decreases in the nearshore region, but increases in the region offshore of the reefs. The measured and modeled sediment dynamics demonstrate a demarcation between the coral reefs sheltered within embayments (Honolua reef) or behind points (Wahikuli reef) and those along the relatively open coastline between Kapalua and Kāʻanapali (Kapalua, Honokeana, Māhinahina, and Honokōwai reefs). The sheltered sites are affected by terrestrial sediment from single stream mouths, where most sediment is delivered within hours of a flood (rain) event. Once this sediment enters the nearshore, it settles out and remains within the reef area for a prolonged period owing to a lack of wave or current-driven bed shear stress. Thus, the primary effect of sediment on the reefs within these sheltered areas is sedimentation. In contrast, coral reefs along the unsheltered (or “open”) section of coastline (between Kapalua and Kāʻanapali) are more exposed to waves and terrestrial sediment from multiple stream sources. At these reefs, fine-grained terrestrial sediment can rarely settle but instead remains in suspension. Thus, even long after a flood event has occurred, these sites chronically experience light attenuation from suspended sediment.
These analyses underscore the importance of understanding how coastal ocean waves and circulation can lead to different sediment dynamics and stressors for coral reefs along the same region of the West Maui coastline. These differing factors indicate that the most effective watershed restoration and mitigation strategies may vary among the different coral reefs and streams. An important next step is to determine how the science of this study can support management goals for these coral reefs: what are target reductions of sedimentation, suspended-sediment concentrations, or the resulting light attenuation? Then, using the coupled hydrodynamic-sediment model, we can examine which watershed restoration scenarios in each stream will best achieve those targets.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221121","collaboration":"Prepared in cooperation with the Deltares Impacts of Extreme Weather Strategic Research Program","programNote":"Coastal and the Marine Hazards and Resources Program","usgsCitation":"Storlazzi, C.D., Cheriton, O.M., Cronin, K.M., van der Heijden, L.H., Winter, G., Rosenberger, K.J., Logan, J.B., and McCall, R.T., 2023, Observations of coastal circulation, waves, and sediment transport along West Maui, Hawaiʻi (November 2017–March 2018), and modeling effects of potential watershed restoration on decreasing sediment loads to adjacent coral reefs: U.S. Geological Survey Open-File Report 2022–1121, 73 p., https://doi.org/10.3133/ofr20221121.","productDescription":"Report: ix, 73 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-138761","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":412766,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P914LMK2","text":"USGS data release","description":"USGS data release","linkHelpText":"Model parameter input files to compare effects of stream discharge scenarios on sediment deposition and concentrations around coral reefs off west Maui, Hawaii"},{"id":412765,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DK9O60","text":"USGS data release","description":"USGS data release","linkHelpText":"Time series data of oceanographic conditions from West Maui, Hawaii, 2017-2018 Coral Reef Circulation and Sediment Dynamics Experiment"},{"id":412764,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1121/ofr20221121.pdf","text":"Report","size":"17.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1121"},{"id":412763,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1121/coverthb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"West Maui","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -156.72475140864907,\n 20.922050876041368\n ],\n [\n -156.5888533113449,\n 20.922050876041368\n ],\n [\n -156.5888533113449,\n 21.0514971765583\n ],\n [\n -156.72475140864907,\n 21.0514971765583\n ],\n [\n -156.72475140864907,\n 20.922050876041368\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Center Director, Pacific Coastal and Marine Science Center
U.S. Geological Survey
2885 Mission Street
Santa Cruz, CA 95060
To constrain fault processes and hazard, fault slip rates may be extrapolated over different fault lengths or time intervals. Here, we investigate slip rates for the Cucamonga Fault (CF). The CF is located at the junction of the Transverse Range fault system with the San Andreas and San Jacinto Faults, and it is hypothesized to connect with these faults, promoting the propagation of large, multi-fault earthquakes. Previous work has shown that CF displacements on late Quaternary alluvial fan surfaces are highly variable along strike. We present two new 10Be surface exposure ages from depth profiles on the alluvial fans. Slip rates are consistent with a rate of 1.4 ± 0.3 m/kyr over time intervals of ∼20, ∼30, and ∼40 kyr. If the CF participates in multi-fault ruptures, then these earthquakes occur either rarely or with sufficient regularity to maintain apparently steady rates over multiple intervals. We also explore along-strike fault displacement variability using a calibrated morphological model. The model successfully reproduces scarp profiles and indicates that fault displacement variability can be explained in part by scarp age but not uplift rate. We infer that both erosion by ephemeral gullying and distributed deformation contribute to fault displacement variability, although both are difficult to detect confidently without excavations across the scarp. These investigations show that better characterization of cumulative-slip variability along strike may improve accuracy and precision of slip rates. Slip rates that do not consider epistemic uncertainties may not be suitable for extrapolation over longer fault sections.
Sediment fingerprinting of fluvial targets has proven useful to guide conservation management and prioritize sediment sources for Federal and State supported programs in the United States. However, the collection and analysis of source samples can make these studies unaffordable, especially when needed for multiple drainage basins. We investigate the potential use of source samples from a basin with similar physiography (using samples from one of a “pair” to evaluate samples from the other) or combined from multiple basins (a “library”).
Source samples from eight basins across six ecoregions were harvested from existing, published studies. Individual source samples were fingerprinted using a mixing model derived from source samples from other basins. The ability to identify source category was evaluated both as part of source verification and by classifying source samples as “targets.”
Approximately half of cropland samples were identified as targets, both as pairs and with the multi-basin source dataset, indicating that cropland samples could be shared for basins in similar ecoregions and be combined for larger stream systems. Streambank samples were better identified with the multi-basin analysis relative to the pairs, and those from mixed land-use basins improved this differentiation except for samples from basins with a dominant land-use type. Inconsistent identification of pasture samples highlighted the need for local samples. Inconsistent identification of forest samples indicated that upland- and riparian-forest samples are distinct. Road samples were identified as both sources and targets, and other source types were rarely apportioned as road: these may have the best potential to supplement local source samples. This source-sample library was then used to improve the accuracy of sediment-source apportionment for a previously studied basin.
Ultimately, the source verification process already used in individual basin studies to evaluate the accuracy of sediment-fingerprinting apportionments was useful for determining how to supplement local source samples with those from other basins. This study shows that supplementing local source samples with those from basins with similar physiography has the potential to both improve fingerprinting accuracy and decrease the cost of this type of study.
Although plant–soil feedbacks (interactions between plants and soils, often mediated by soil microbes, abbreviated as PSFs) are widely known to influence patterns of plant diversity at local and landscape scales, these interactions are rarely examined in the context of important environmental factors. Resolving the roles of environmental factors is important because the environmental context may alter PSF patterns by modifying the strength or even direction of PSFs for certain species. One important environmental factor that is increasing in scale and frequency with climate change is fire, though the influence of fire on PSFs remains essentially unexamined. By changing microbial community composition, fire may alter the microbes available to colonize the roots of plants and thus seedling growth post-fire. This has potential to change the strength and/or direction of PSFs, depending on how such changes in microbial community composition occur and the plant species with which the microbes interact. We examined how a recent fire altered PSFs of two leguminous, nitrogen-fixing tree species in Hawaiʻi. For both species, growing in conspecific soil resulted in higher plant performance (as measured by biomass production) than growing in heterospecific soil. This pattern was mediated by nodule formation, an important process for growth for legume species. Fire weakened PSFs for these species and therefore pairwise PSFs, which were significant in unburned soils, but were nonsignificant in burned soils. Theory suggests that positive PSFs such as those found in unburned sites would reinforce the dominance of species where they are locally dominant. The change in pairwise PSFs with burn status shows PSF-mediated dominance might diminish after fire. Our results demonstrate that fire can modify PSFs by weakening the legume-rhizobia symbiosis, which may alter local competitive dynamics between two canopy dominant tree species. These findings illustrate the importance of considering environmental context when evaluating the role of PSFs for plants.
Detecting spatiotemporal changes in the abundances of organisms is key to effectively conserving species. While indices of abundance have long been used, there has been a shift toward model-based estimators that account for the detection process. Popular approaches including traditional occupancy models and N-mixture models entail tradeoffs. The traditional occupancy approach requires the researcher coarsen the characterization of abundance to the probability that a site is occupied or unoccupied. Conversely, N-mixture models make use of variation in counts, but perform poorly when individuals have low detectability or move into or out of sites between visits. Multistate occupancy models that differentiate relatively abundant from non-abundant states have the potential to fill this gap but have been underexplored. We conducted a simulation study to test whether multistate occupancy models could capture spatial abundance patterns and detect population declines in the face of low individual detection probability (p ≤ 0.3) and unmodeled heterogeneity (e.g., that arising from individual movement). We considered 10,773 scenarios to examine the effects of differing amounts of heterogeneity as well as alternative study designs, population parameters, and modeling choices. We tracked bias in the proportion of sites estimated to be in the abundant state for single-season models, and power to detect a declining trend across multiple years. We also evaluated data diagnostic metrics to provide guidance to users. Multistate occupancy models were able to differentiate sites with higher abundances from sites with lower abundances when there were at least medium levels of spatial heterogeneity in true abundances. If different sites were randomly selected each year, power to detect even large population declines (65%) was poor (power < 0.8). However, if the same sites were surveyed each year, and a dynamic multistate occupancy was used, multistate occupancy models could detect (power ≥ 0.8) relatively small declines (5-40%) in 20% of scenarios, and frequently detect large declines of 45-60% (mean power = 0.92). Conservation decisions rely on detecting change reliably, rarely needing absolute abundance information. Multistate occupancy models can improve our ability to detect changing abundance while accommodating low individual detection probability and heterogeneity in count monitoring data.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2023.110303","usgsCitation":"Steen, V., Duarte, A., and Peterson, J., 2023, An evaluation of multistate occupancy models for estimating relative abundance and population trends: Ecological Modelling, v. 478, 110303, 9 p., https://doi.org/10.1016/j.ecolmodel.2023.110303.","productDescription":"110303, 9 p.","ipdsId":"IP-144901","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":444548,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolmodel.2023.110303","text":"Publisher Index Page"},{"id":429891,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"478","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Steen, Valerie A. 0000-0002-1417-8139","orcid":"https://orcid.org/0000-0002-1417-8139","contributorId":205994,"corporation":false,"usgs":false,"family":"Steen","given":"Valerie A.","affiliations":[{"id":36710,"text":"University of Connecticut","active":true,"usgs":false}],"preferred":false,"id":902764,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duarte, Adam","contributorId":337608,"corporation":false,"usgs":false,"family":"Duarte","given":"Adam","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":902765,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, James T. 0000-0002-7709-8590 james_peterson@usgs.gov","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":2111,"corporation":false,"usgs":true,"family":"Peterson","given":"James","email":"james_peterson@usgs.gov","middleInitial":"T.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":902766,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70242017,"text":"70242017 - 2023 - Six years of fluvial response to a large dam removal on the Carmel River, California, USA","interactions":[],"lastModifiedDate":"2023-06-27T16:51:13.29324","indexId":"70242017","displayToPublicDate":"2023-02-07T08:17:40","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"Six years of fluvial response to a large dam removal on the Carmel River, California, USA","docAbstract":"Measuring river response to dam removal affords a rare, important opportunity to study fluvial response to sediment pulses on a large field scale. We present a before–after/control–impact study of the Carmel River, California, measuring fluvial geomorphic and grain-size evolution over 8 years, six of which postdated removal of a 32 m-high dam (one of the largest dams removed worldwide) and included 11 flow events exceeding the 2-year flood magnitude. We find that the reservoir-sediment pulse following dam removal was relatively small (97 000 ± 24 000 t over 4 years), owing to deliberate reservoir-sediment stabilization. Scaled to the size of the Carmel River watershed and compared against long-term bedrock denudation rates, the post-dam-removal sediment release was slightly less than the annualized long-term sediment export from this basin. New sediment transited >30 km to the river mouth in less than 2 years, assisted by floods 2 and 4 years after dam removal. The sediment pulse fined the downstream riverbed while causing mostly low-magnitude bed-elevation changes: commonly 0.5 to 1 m or smaller, occurring as discontinuous sediment patches or interstitial deposits, aside from the filling and subsequent partial scour of deep pools. There was no major geomorphic reset downstream from the dam site. Geomorphic changes were driven almost entirely by flow rather than by the modest increase in sediment supply, in contrast to recent examples from other large dam removals. The relatively minor disturbance caused by dam removal on the Carmel River is likely analogous to many future dam removals: a relatively small sediment pulse after deliberate limitation of reservoir-sediment erosion, and with an upstream dam remaining in place. Thus, a large dam removal need not lead to major downstream impacts.
","language":"English","publisher":"Wiley","doi":"10.1002/esp.5561","usgsCitation":"East, A.E., Harrison, L.R., Smith, D.P., Logan, J.B., and Bond, R., 2023, Six years of fluvial response to a large dam removal on the Carmel River, California, USA: Earth Surface Processes and Landforms, v. 48, no. 8, p. 1487-1501, https://doi.org/10.1002/esp.5561.","productDescription":"15 p.","startPage":"1487","endPage":"1501","ipdsId":"IP-146182","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":444561,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/esp.5561","text":"Publisher Index Page"},{"id":415160,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Carmel River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.62577522165773,\n 36.36105374788829\n ],\n [\n -121.58724434610056,\n 36.40905968854021\n ],\n [\n -121.61843600726598,\n 36.46293870830412\n ],\n [\n -121.750541866319,\n 36.53594773620604\n ],\n [\n -121.8532908678046,\n 36.61551540625254\n ],\n [\n -121.90741757394426,\n 36.588265345279865\n ],\n [\n -121.93261880515863,\n 36.54806169091083\n ],\n [\n -121.88413981443409,\n 36.520771377870034\n ],\n [\n -121.79850327393265,\n 36.50047233869989\n ],\n [\n -121.7839886060512,\n 36.469663631874326\n ],\n [\n -121.74712134963194,\n 36.40894399256348\n ],\n [\n -121.68209557572075,\n 36.36804816657221\n ],\n [\n -121.62606895769773,\n 36.3610353099688\n ],\n [\n -121.62577522165773,\n 36.36105374788829\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"48","issue":"8","noUsgsAuthors":false,"publicationDate":"2023-02-24","publicationStatus":"PW","contributors":{"authors":[{"text":"East, Amy E. 0000-0002-9567-9460 aeast@usgs.gov","orcid":"https://orcid.org/0000-0002-9567-9460","contributorId":196364,"corporation":false,"usgs":true,"family":"East","given":"Amy","email":"aeast@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":868544,"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":868545,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, Douglas P.","contributorId":201716,"corporation":false,"usgs":false,"family":"Smith","given":"Douglas","email":"","middleInitial":"P.","affiliations":[{"id":35924,"text":"California State University, Monterey Bay","active":true,"usgs":false}],"preferred":false,"id":868546,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Logan, Joshua B. 0000-0002-6191-4119 jlogan@usgs.gov","orcid":"https://orcid.org/0000-0002-6191-4119","contributorId":2335,"corporation":false,"usgs":true,"family":"Logan","given":"Joshua","email":"jlogan@usgs.gov","middleInitial":"B.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":868547,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bond, Rosealea","contributorId":201717,"corporation":false,"usgs":false,"family":"Bond","given":"Rosealea","affiliations":[{"id":12520,"text":"NOAA National Marine Fisheries Service","active":true,"usgs":false}],"preferred":false,"id":868548,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70256526,"text":"70256526 - 2023 - Predicting habitat and distribution of an interior highlands regional endemic winter stonefly (Allocapnia mohri) in Arkansas using random forest models","interactions":[],"lastModifiedDate":"2024-08-22T11:07:08.990132","indexId":"70256526","displayToPublicDate":"2023-02-06T11:29:07","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":18324,"text":"Hydrobiology","active":true,"publicationSubtype":{"id":10}},"title":"Predicting habitat and distribution of an interior highlands regional endemic winter stonefly (Allocapnia mohri) in Arkansas using random forest models","docAbstract":"Stoneflies are a globally threatened aquatic insect order. In Arkansas, a diverse group of winter stonefly (Capniidae: Allocapnia) have not been surveyed since the 1980s, likely because species-level identification requires the rarely-collected adult form. Allocapnia mohri, a regional endemic, was previously commonly found in mountainous, intermittent streams from the Ouachita Mountains ecoregion north to the Ozark Highlands, but no species distributional models including land use or climate variables exist to our knowledge. We collected adults from 71 stream reaches from the historic Arkansas range from November to April 2020 and 2021. We modeled distributions using random forest (RF) models populated with landscape, climate, and both data to determine which were most predictive of species presence. Correlations between landscape or climate variables and presence were examined using multiple logistic regression. The landscape RF models performed better than the climate or landscape + climate RF models. A. mohri presence sites tended to have a greater elevation, a lower mean July temperature, and a greater percentage of very slow infiltration soils in the watershed, compared to absence sites. A. mohri was absent at the Ouachita Mountains sites and may be experiencing a range contraction or migration northward.
","language":"English","publisher":"MDPI","doi":"10.3390/hydrobiology2010013","usgsCitation":"Annaratone, B., Larson, C., Prater, C., Dowling, A., Magoulick, D.D., and Evans-White, M.A., 2023, Predicting habitat and distribution of an interior highlands regional endemic winter stonefly (Allocapnia mohri) in Arkansas using random forest models: Hydrobiology, v. 2, no. 1, p. 196-211, https://doi.org/10.3390/hydrobiology2010013.","productDescription":"16 p.","startPage":"196","endPage":"211","ipdsId":"IP-148164","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":444581,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/hydrobiology2010013","text":"Publisher Index Page"},{"id":433011,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -91.01694463644638,\n 36.49973760721201\n ],\n [\n -94.64880267254001,\n 36.47406350173745\n ],\n [\n -94.42836217709565,\n 35.38954127077116\n ],\n [\n -94.47263609475836,\n 33.690237372303145\n ],\n [\n -93.92329526083718,\n 33.50875350986783\n ],\n [\n -93.15882305498532,\n 33.95704519399207\n ],\n [\n -92.44070215460535,\n 34.644196725743356\n ],\n [\n -91.1183522293626,\n 35.76377161713451\n ],\n [\n -91.01694463644638,\n 36.49973760721201\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"2","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-02-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Annaratone, Brianna","contributorId":341024,"corporation":false,"usgs":false,"family":"Annaratone","given":"Brianna","email":"","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":907818,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Larson, Camryn","contributorId":341025,"corporation":false,"usgs":false,"family":"Larson","given":"Camryn","email":"","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":907819,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Prater, Clay","contributorId":341026,"corporation":false,"usgs":false,"family":"Prater","given":"Clay","email":"","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":907820,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dowling, Ashley","contributorId":341027,"corporation":false,"usgs":false,"family":"Dowling","given":"Ashley","email":"","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":907821,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Magoulick, Daniel D. 0000-0001-9665-5957 danmag@usgs.gov","orcid":"https://orcid.org/0000-0001-9665-5957","contributorId":2513,"corporation":false,"usgs":true,"family":"Magoulick","given":"Daniel","email":"danmag@usgs.gov","middleInitial":"D.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":907822,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Evans-White, Michelle A.","contributorId":341028,"corporation":false,"usgs":false,"family":"Evans-White","given":"Michelle","email":"","middleInitial":"A.","affiliations":[{"id":6623,"text":"University of Arkansas","active":true,"usgs":false}],"preferred":false,"id":907823,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70240729,"text":"70240729 - 2023 - A restructured Bayesian approach to estimate the abundance of a rare and invasive fish","interactions":[],"lastModifiedDate":"2023-05-25T15:45:17.03652","indexId":"70240729","displayToPublicDate":"2023-02-04T07:20:54","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"A restructured Bayesian approach to estimate the abundance of a rare and invasive fish","docAbstract":"Quantifying invasive species abundance informs management and control strategies. However, estimating abundance can be challenging, particularly when dealing with rare species early in the invasion process. Data generated from control strategies, such as removing invasive species, are usually not suited to conventional statistical modelling approaches. Hence, we developed a Bayesian model using data generated by a grass carp (Ctenopharyngodon idella) control program in the Sandusky River, Ohio (USA) for estimating the abundance of rare, invasive species. The model is a restructured N-mixture model modified to incorporate the data generating process (i.e., setting a trammel net to isolate a sampling area followed by boat-mounted electrofishing). Allowing the estimation of grass carp abundance from the species removal data, which had very few detections relative to the sampling effort. Our results indicated that the average number of grass carp present in the river at any one time did not change substantially from 2018 to 2020. The highest abundance estimates were in the lower and upper-middle segments of the river, suggesting possible recolonization from Lake Erie, and possibly other tributaries. Ultimately, the ability to use species-control data to estimate abundance provides important information for management, particularly for invasive ‘sleeper’ species in freshwater ecosystems.
The lampricides TFM (3-trifluoromethyl-4′-nitrophenol) and Niclosamide (NIC, 2′, 5-dichloro-4′-nitrosalicylanilide) are used to control sea lamprey populations in the Great Lakes and associated tributaries. Niclosamide is often used as an additive to TFM to reduce the amount of TFM required to control sea lamprey. Concern is growing over the risk that lampricide treatments pose to native freshwater mussels residing in streams. Our objectives were to determine the acute toxicity of TFM and TFM:NIC to free glochidia (removed from the marsupial gills), compare the relative toxicity of TFM and TFM:NIC between free glochidia and brooded glochidia (within the marsupial gills), determine if glochidia age influences toxicity, and assess if exposure of gravid mussels to TFM and TFM:NIC alters behavior and reproduction. Three acute toxicity tests (2:TFM, 1:TFM : NIC) were conducted with glochidia and adults of the plain pocketbook mussel (Lampsilis cardium). In tests with glochidia, viability did not differ across TFM and TFM : NIC concentrations that encompassed typical stream treatments. Glochidia age influenced toxicity as glochidia obtained later in the brooding season were less viable than glochidia obtained earlier in the brooding season. Exposure of adults to elevated concentrations of lampricides often resulted in behavioral effects, but rarely affected reproductive endpoints. Because mussels are long-lived (30 to 100 y), even intermittent and short duration exposures may cumulatively affect mussels over their lifetime. The risks posed by lampricide treatments in the Great Lakes would be further informed by research on the sublethal effects of lampricides, particularly effects on non-target organisms such as mussels.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2022.11.007","usgsCitation":"Newton, T., Boogaard, M.A., Schloesser, N., Kirkeeng, C., Schueller, J., and Toribio, S.G., 2023, Behavioral and reproductive effects of the lampricides TFM and TFM:1% Niclosamide on native freshwater mussels: Journal of Great Lakes Research, v. 49, no. 1, p. 303-317, https://doi.org/10.1016/j.jglr.2022.11.007.","productDescription":"15 p.","startPage":"303","endPage":"317","ipdsId":"IP-140007","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":444639,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2022.11.007","text":"Publisher Index Page"},{"id":435474,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9A12ZR4","text":"USGS data release","linkHelpText":"Behavioral and Reproductive Effects of the Lampricides TFM and TFM:1% Niclosamide on Native Freshwater Mussels - SPSS Code Release"},{"id":413949,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"49","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Newton, Teresa J. 0000-0001-9351-5852","orcid":"https://orcid.org/0000-0001-9351-5852","contributorId":78696,"corporation":false,"usgs":true,"family":"Newton","given":"Teresa J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":866110,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Boogaard, Michael A. 0000-0002-5192-8437 mboogaard@usgs.gov","orcid":"https://orcid.org/0000-0002-5192-8437","contributorId":865,"corporation":false,"usgs":true,"family":"Boogaard","given":"Michael","email":"mboogaard@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":866111,"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":866112,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"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":866113,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"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":866114,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Toribio, Sherwin G.","contributorId":302983,"corporation":false,"usgs":false,"family":"Toribio","given":"Sherwin","email":"","middleInitial":"G.","affiliations":[{"id":47908,"text":"University of Wisconsin - La Crosse","active":true,"usgs":false}],"preferred":false,"id":866115,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70240689,"text":"70240689 - 2023 - Wild rodents harbor high diversity of Arthroderma","interactions":[],"lastModifiedDate":"2023-02-15T13:03:36.894491","indexId":"70240689","displayToPublicDate":"2023-02-01T07:02:16","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5648,"text":"Persoonia - Molecular Phylogeny and Evolution of Fungi","active":true,"publicationSubtype":{"id":10}},"title":"Wild rodents harbor high diversity of Arthroderma","docAbstract":"Reported here are geological, geophysical, mineralogical, and geochemical data on a previously unknown trachyte-hosted rare earth element (REE)-Nb-Zr occurrence at Pennington Mountain in northern Maine, USA. This occurrence was newly discovered by a regional multiparameter, airborne radiometric survey that revealed anomalously high equivalent Th (eTh) and U (eU), confirmed by a detailed ground radiometric survey and by portable X-Ray fluorescence (pXRF) and whole-rock analyses of representative rock samples. The mineralized area occurs within an elongate trachyte body (~1.2 km2) that intrudes Ordovician volcanic rocks. Geologic constraints suggest that the trachyte is also Ordovician in age. The eastern lobe (~900 × ~400 m) of the trachyte is pervasively brecciated with a matrix containing seams, lenses, and veinlets composed mainly of potassium feldspar, albite, and fine-grained zircon and monazite. Barite is locally abundant. Minor minerals within the matrix include columbite, bastnäsite, euxenite, chlorite, pyrite, sphalerite, and magnetite. The pXRF analyses of 22 samples (App. Table A1) collected from the eastern lobe demonstrate that this entire part of the trachyte is highly mineralized. Whole-rock geochemical analyses for samples from the eastern lobe document high average contents of Zr (1.17 wt %), Nb (1,656 ppm), Ba (3,132 ppm), Y (1,140 ppm), Hf (324 ppm), Ta (122 ppm), Th (124 ppm), U (36.5 ppm), Zn (689 ppm), and Sn (106 ppm). Among light REE, the highest average concentrations are shown by La (763 ppm) and Ce (1,479 ppm). For heavy REE (HREE), Dy and Er are the most abundant on average (167 and 114 ppm, respectively). No HREE-rich minerals such as xenotime have been identified; the HREE may reside chiefly in monazite and bastnäsite, and within the fine-grained zircon. Very strong positive correlations (R2) of 0.92 to 0.98 exist between Th and Zr, Nb, Y, Ce, Yb, and Sn, indicating that the radiometric data for eTh are valid proxies for concentrations of these metals in the mineralized rocks.
","language":"English","publisher":"Society for Economic Geologists","doi":"10.5382/econgeo.4993","usgsCitation":"Wang, C., Slack, J.F., Shah, A.K., Yates, M.G., Lentz, D.R., Whittaker, A.T., and Marvinney, R.G., 2023, A recently discovered trachyte-hosted rare earth element-niobium-zirconium occurrence in northern Maine, USA: Economic Geology, v. 118, no. 1, p. 1-13, https://doi.org/10.5382/econgeo.4993.","productDescription":"13 p.","startPage":"1","endPage":"13","ipdsId":"IP-143441","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":444655,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5382/econgeo.4993","text":"Publisher Index Page"},{"id":418496,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maine","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -67.73204287455003,\n 45.7741074745758\n ],\n [\n -67.73204287455003,\n 47.52268519237853\n ],\n [\n -70.36739756743843,\n 47.52268519237853\n ],\n [\n -70.36739756743843,\n 45.7741074745758\n ],\n [\n -67.73204287455003,\n 45.7741074745758\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"118","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wang, Chunzeng 0000-0002-8362-5174","orcid":"https://orcid.org/0000-0002-8362-5174","contributorId":295415,"corporation":false,"usgs":false,"family":"Wang","given":"Chunzeng","email":"","affiliations":[{"id":63866,"text":"University of Maine at Presque-Isle","active":true,"usgs":false}],"preferred":false,"id":876284,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Slack, John F. 0000-0001-6600-3130 jfslack@usgs.gov","orcid":"https://orcid.org/0000-0001-6600-3130","contributorId":1032,"corporation":false,"usgs":true,"family":"Slack","given":"John","email":"jfslack@usgs.gov","middleInitial":"F.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":876285,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shah, Anjana K. 0000-0002-3198-081X ashah@usgs.gov","orcid":"https://orcid.org/0000-0002-3198-081X","contributorId":2297,"corporation":false,"usgs":true,"family":"Shah","given":"Anjana","email":"ashah@usgs.gov","middleInitial":"K.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":876286,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yates, Martin G.","contributorId":313571,"corporation":false,"usgs":false,"family":"Yates","given":"Martin","email":"","middleInitial":"G.","affiliations":[{"id":7063,"text":"University of Maine","active":true,"usgs":false}],"preferred":false,"id":876287,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lentz, David R.","contributorId":313573,"corporation":false,"usgs":false,"family":"Lentz","given":"David","email":"","middleInitial":"R.","affiliations":[{"id":18889,"text":"University of New Brunswick","active":true,"usgs":false}],"preferred":false,"id":876288,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Whittaker, Amber T.H.","contributorId":313574,"corporation":false,"usgs":false,"family":"Whittaker","given":"Amber","email":"","middleInitial":"T.H.","affiliations":[{"id":7257,"text":"Maine Geological Survey","active":true,"usgs":false}],"preferred":false,"id":876289,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Marvinney, Robert G.","contributorId":131130,"corporation":false,"usgs":false,"family":"Marvinney","given":"Robert","email":"","middleInitial":"G.","affiliations":[{"id":7257,"text":"Maine Geological Survey","active":true,"usgs":false}],"preferred":false,"id":876290,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70251801,"text":"70251801 - 2023 - Timing of rhyolite intrusion and Carlin-type gold mineralization at the Cortez Hills Carlin-type deposit, Nevada, USA","interactions":[],"lastModifiedDate":"2024-02-29T12:41:18.941661","indexId":"70251801","displayToPublicDate":"2023-02-01T06:37:07","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1472,"text":"Economic Geology","active":true,"publicationSubtype":{"id":10}},"title":"Timing of rhyolite intrusion and Carlin-type gold mineralization at the Cortez Hills Carlin-type deposit, Nevada, USA","docAbstract":"Carlin-type gold deposits (CTDs) of Nevada are the largest producers of gold in the United States, a leader in world gold production. Although much has been resolved about the characteristics and origin of CTDs in Nevada, major questions remain, especially about (1) the role of magmatism, whether only a source of heat or also metals, (2) whether CTDs only formed in the Eocene, and (3) whether pre-Eocene metal concentrations contributed to Eocene deposits. These issues are exemplified by the CTDs of the Cortez region, the second largest concentration of these deposits after the Carlin trend.
Carlin-type deposits are notoriously difficult to date because they rarely generate dateable minerals. An age can be inferred from crosscutting relationships with dated dikes and other intrusions, which we have done for the giant Cortez Hills CTD. What we term “Cortez rhyolites” consist of two petrographic-geochemical groups of siliceous dikes: (1) quartz-sanidine-plagioclase-biotite-phyric, high-SiO2 rhyolites emplaced at 35.7 Ma based on numerous 40Ar/39Ar dates and (2) plagioclase-biotite-quartz ± hornblende-phyric, low-SiO2 rhyolites, which probably were emplaced at the same time but possibly as early as ~36.2 Ma. The dikes form a NNW-trending belt that is ~6 to 10 km wide × 40 km long and centered on the Cortez Hills deposit, and they require an underlying felsic pluton that fed the dikes. Whether these dikes pre- or postdated mineralization has been long debated. We show that dike emplacement spanned the time of mineralization. Many of both high- and low-SiO2 dikes are altered and mineralized, although none constitute ore. In altered-mineralized dikes, plagioclase has been replaced by kaolinite and calcite, and biotite by smectite, calcite, and marcasite. Sanidine is unaltered except in a few samples that are completely altered to quartz and kaolinite. Sulfides present in mineralized dikes are marcasite, pyrite, arsenopyrite, and As-Sb–bearing pyrite. Mineralized dikes are moderately enriched in characteristic Carlin-type elements (Au, Hg, Sb, Tl, As, and S), as well as elements found in some CTDs (Ag, Bi, Cu, Mo), and variably depleted in MgO, CaO, Na2O, K2O, MnO, Rb, Sr, and Ba. In contrast, some high-SiO2 rhyolites are unaltered and cut high-grade ore, which shows that they are post-ore. Both mineralized and post-ore dikes have indistinguishable sanidine 40Ar/39Ar dates. These characteristics, along with published interpretations that other giant CTDs formed in a few tens of thousands of years, indicate the Cortez Hills CTD formed at 35.7 Ma. All Cortez-area CTDs are in or adjacent to the Cortez rhyolite dike swarm, which suggests that the felsic pluton that fed the dikes was the hydrothermal heat source. Minor differences in alteration and geochemistry between dikes and typical Paleozoic sedimentary rock-hosted ore probably reflect low permeability and low reactivity of the predominantly quartzofeldspathic dikes.
Despite widespread pre-35.7 Ma mineralization in the Cortez region, including deposits near several CTDs, we find no evidence that older deposits or Paleozoic basinal rocks contributed metals to Cortez-area CTDs. Combining our new information about the age of Cortez Hills with published and our dates on other CTDs demonstrates that CTD formation coincided with the southwestern migration of magmatism across Nevada, supporting a genetic relationship to Eocene magmatism. CTDs are best developed where deep-seated (~6–8 km), probably granitic plutons, expressed in deposits only as dikes, established large, convective hydrothermal systems.
Resource managers have rarely accounted for evolutionary dynamics in the design or implementation of climate change adaptation strategies. We brought the research and management communities together to identify challenges and opportunities for applying evidence from evolutionary science to support on-the-ground actions intended to enhance species' evolutionary potential. We amalgamated input from natural-resource practitioners and interdisciplinary scientists to identify information needs, current knowledge that can fill those needs, and future avenues for research. Three focal areas that can guide engagement include: (1) recognizing when to act, (2) understanding the feasibility of assessing evolutionary potential, and (3) identifying best management practices. Although researchers commonly propose using molecular methods to estimate genetic diversity and gene flow as key indicators of evolutionary potential, we offer guidance on several additional attributes (and their proxies) that may also guide decision-making, particularly in the absence of genetic data. Finally, we outline existing decision-making frameworks that can help managers compare alternative strategies for supporting evolutionary potential, with the goal of increasing the effective use of evolutionary information, particularly for species of conservation concern. We caution, however, that arguing over nuance can generate confusion; instead, dedicating increased focus on a decision-relevant evidence base may better lend itself to climate adaptation actions.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/csp2.12855","usgsCitation":"Thompson, L., Thurman, L., Cook, C.N., Beever, E.A., Sgro, C., Battles, A., Botero, C., Gross, J.E., Hall, K., Hendry, A.P., Hoffmann, A., Hoving, C., LeDee, O.E., Mengelt, C., Nicotra, A., Niver, R., Pérez-Jvostov, F., Quiñones, R., Schuurman, G.W., Schwartz, M.K., Szymanski, J., and Whiteley, A., 2023, Connecting research and practice to enhance the evolutionary potential of species under climate change: Conservation Science and Practice, v. 5, no. 2, e12855, 18 p., https://doi.org/10.1111/csp2.12855.","productDescription":"e12855, 18 p.","ipdsId":"IP-134501","costCenters":[{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":487930,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/csp2.12855","text":"Publisher Index Page"},{"id":485334,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"5","issue":"2","noUsgsAuthors":false,"publicationDate":"2023-01-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Thompson, Laura 0000-0002-7884-6001","orcid":"https://orcid.org/0000-0002-7884-6001","contributorId":212190,"corporation":false,"usgs":true,"family":"Thompson","given":"Laura","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true}],"preferred":true,"id":935461,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Thurman, Lindsey 0000-0003-3142-4909","orcid":"https://orcid.org/0000-0003-3142-4909","contributorId":269425,"corporation":false,"usgs":true,"family":"Thurman","given":"Lindsey","email":"","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":935462,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cook, Carly N.","contributorId":204315,"corporation":false,"usgs":false,"family":"Cook","given":"Carly","email":"","middleInitial":"N.","affiliations":[{"id":36914,"text":"School of Biological Sciences, Monash University, Clayton, Victoria 3800, Australia","active":true,"usgs":false}],"preferred":false,"id":935463,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Beever, Erik A. 0000-0002-9369-486X ebeever@usgs.gov","orcid":"https://orcid.org/0000-0002-9369-486X","contributorId":2934,"corporation":false,"usgs":true,"family":"Beever","given":"Erik","email":"ebeever@usgs.gov","middleInitial":"A.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":935464,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sgro, Carla","contributorId":354351,"corporation":false,"usgs":false,"family":"Sgro","given":"Carla","affiliations":[],"preferred":false,"id":935465,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Battles, Andrew","contributorId":354352,"corporation":false,"usgs":false,"family":"Battles","given":"Andrew","affiliations":[],"preferred":false,"id":935466,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Botero, Carlos","contributorId":354353,"corporation":false,"usgs":false,"family":"Botero","given":"Carlos","affiliations":[],"preferred":false,"id":935467,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Gross, John E.","contributorId":106777,"corporation":false,"usgs":false,"family":"Gross","given":"John","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":935468,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hall, Kimberley","contributorId":354354,"corporation":false,"usgs":false,"family":"Hall","given":"Kimberley","affiliations":[],"preferred":false,"id":935469,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hendry, Andrew P.","contributorId":178839,"corporation":false,"usgs":false,"family":"Hendry","given":"Andrew","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":935470,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Hoffmann, Ary","contributorId":354355,"corporation":false,"usgs":false,"family":"Hoffmann","given":"Ary","affiliations":[],"preferred":false,"id":935471,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Hoving, Christopher","contributorId":289379,"corporation":false,"usgs":false,"family":"Hoving","given":"Christopher","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":935472,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"LeDee, Olivia E. 0000-0002-7791-5829 oledee@usgs.gov","orcid":"https://orcid.org/0000-0002-7791-5829","contributorId":242820,"corporation":false,"usgs":true,"family":"LeDee","given":"Olivia","email":"oledee@usgs.gov","middleInitial":"E.","affiliations":[{"id":65882,"text":"Midwest Climate Adaptation Science Center","active":true,"usgs":true}],"preferred":true,"id":935473,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Mengelt, Claudia 0000-0001-7869-5170","orcid":"https://orcid.org/0000-0001-7869-5170","contributorId":304087,"corporation":false,"usgs":true,"family":"Mengelt","given":"Claudia","email":"","affiliations":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"preferred":true,"id":935474,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Nicotra, Adrienne","contributorId":147686,"corporation":false,"usgs":false,"family":"Nicotra","given":"Adrienne","affiliations":[{"id":16897,"text":"Division of Evolution, Ecology and Genetics, Research School of Biology, Australian National University, Canberra","active":true,"usgs":false}],"preferred":false,"id":935475,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Niver, Robin A.","contributorId":354356,"corporation":false,"usgs":false,"family":"Niver","given":"Robin A.","affiliations":[],"preferred":false,"id":935476,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Pérez-Jvostov, Felipe","contributorId":354357,"corporation":false,"usgs":false,"family":"Pérez-Jvostov","given":"Felipe","affiliations":[],"preferred":false,"id":935477,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Quiñones, Rebecca M.","contributorId":354358,"corporation":false,"usgs":false,"family":"Quiñones","given":"Rebecca M.","affiliations":[],"preferred":false,"id":935478,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Schuurman, Gregor W. 0000-0002-9304-7742","orcid":"https://orcid.org/0000-0002-9304-7742","contributorId":147698,"corporation":false,"usgs":false,"family":"Schuurman","given":"Gregor","email":"","middleInitial":"W.","affiliations":[{"id":16909,"text":"U.S. National Park Service, Natural Resource Stewardship and Science, Fort Collins, CO, 80525, USA","active":true,"usgs":false}],"preferred":false,"id":935479,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Schwartz, Michael K.","contributorId":199035,"corporation":false,"usgs":false,"family":"Schwartz","given":"Michael","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":935480,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Szymanski, Jennifer","contributorId":15123,"corporation":false,"usgs":false,"family":"Szymanski","given":"Jennifer","affiliations":[{"id":6969,"text":"U.S. Fish and Wildlife Service, Division of Endangered Species","active":true,"usgs":false}],"preferred":false,"id":935481,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Whiteley, Andrew R.","contributorId":286853,"corporation":false,"usgs":false,"family":"Whiteley","given":"Andrew R.","affiliations":[{"id":36523,"text":"University of Montana","active":true,"usgs":false}],"preferred":false,"id":935482,"contributorType":{"id":1,"text":"Authors"},"rank":22}]}} ,{"id":70241055,"text":"70241055 - 2023 - Recent and future declines of a historically widespread pollinator linked to climate, land cover, and pesticides","interactions":[],"lastModifiedDate":"2023-03-08T13:17:43.875878","indexId":"70241055","displayToPublicDate":"2023-01-23T07:11:57","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3164,"text":"Proceedings of the National Academy of Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Recent and future declines of a historically widespread pollinator linked to climate, land cover, and pesticides","docAbstract":"Non-native species maps are important tools for understanding and managing biological invasions. We demonstrate a novel approach to extend presence modeling to map fractional cover (FC) of non-native yellow sweet clover Melilotus officinalis in the Northern Great Plains, USA. We used ensembles of MaxEnt models to map FC across landscapes from satellite imagery trained from regional aerial imagery that was trained by local unmanned aerial vehicle (UAV) imagery. Clover cover from field surveys and classified UAV imagery were nearly identical (n = 22, R2 = 0.99). Two classified UAV images provided training data to map clover presence with MaxEnt and National Agricultural Imagery Program (NAIP) aerial imagery. We binned cover predictions from NAIP imagery within each Sentinel-2 pixel into eight cover classes to create pure (100%) and FC (20%–95%) training data and modeled each class separately using MaxEnt and Sentinel-2 imagery. We mapped pure clover with one classification threshold and compared its performance to 15 candidate maps that included FC predictions outside pure predictions. Each FC map represented alternative combinations of five MaxEnt thresholds and three approaches to assign cover to pixels with multiple predictions from the FC ensemble. Evaluations of performance with independent datasets revealed maps including FC corresponded to field (n = 32, R2 range: 0.39–0.68) and UAV (n = 20, R2 range: 0.61–0.84) data better than pure clover maps (R2 = 0.15 and 0.31, respectively). Overall, the pure clover map predicted 3.2% cover, whereas the three best performing FC maps predicted 6.6%–8.0% cover. Including FC predictions increased accuracy and cover predictions which can improve ecological understanding of invasions. Our method allows efficient FC mapping for vegetative species discernible in UAV imagery and may be especially useful for mapping rare, irruptive or patchily distributed species with poor representation in field data, which challenges landscape-level mapping.
","language":"English","publisher":"Wiley","doi":"10.1002/rse2.325","usgsCitation":"Preston, T.M., Johnston, A.N., Ebenhoch, K.G., and Diehl, R.H., 2023, Beyond presence mapping: Predicting fractional cover of non-native vegetation in Sentinel-2 imagery using an ensemble of MaxEnt models: Remote Sensing in Ecology and Conservation, v. 9, no. 4, p. 512-526, https://doi.org/10.1002/rse2.325.","productDescription":"15 p.","startPage":"512","endPage":"526","ipdsId":"IP-135782","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":444792,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/rse2.325","text":"Publisher Index Page"},{"id":435499,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91X4EPQ","text":"USGS data release","linkHelpText":"Fractional cover estimates of sweet clover derived from UAV, aerial, and Sentinel-2 imagery for central Montana and northwest South Dakota, 2019"},{"id":412216,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, South Dakota","county":"Butte County, Harding County, Musselshell County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-108.6276,46.7487],[-108.5911,46.7491],[-108.5699,46.7489],[-108.5494,46.7486],[-108.5163,46.7481],[-108.4991,46.7483],[-108.4772,46.7489],[-108.4567,46.7491],[-108.3586,46.7503],[-108.3373,46.7509],[-108.2618,46.7524],[-108.2399,46.7525],[-108.2187,46.7526],[-108.1981,46.7532],[-108.1763,46.7533],[-107.8833,46.7568],[-107.878,46.7571],[-107.8276,46.7566],[-107.8279,46.7502],[-107.8221,46.7455],[-107.8162,46.7444],[-107.8164,46.7394],[-107.8118,46.7375],[-107.8101,46.7306],[-107.8155,46.7279],[-107.8295,46.7254],[-107.829,46.7218],[-107.8212,46.7184],[-107.8287,46.7126],[-107.819,46.7083],[-107.8238,46.7038],[-107.8186,46.7],[-107.811,46.693],[-107.8043,46.6942],[-107.8026,46.6873],[-107.7955,46.6835],[-107.7957,46.6784],[-107.7998,46.6758],[-107.802,46.6707],[-107.7983,46.6643],[-107.8024,46.6602],[-107.8044,46.6607],[-107.8082,46.6631],[-107.8111,46.659],[-107.8073,46.6539],[-107.8153,46.6522],[-107.8217,46.6413],[-107.8224,46.639],[-107.8152,46.638],[-107.8148,46.6325],[-107.8184,46.6243],[-107.829,46.6231],[-107.8298,46.6204],[-107.8188,46.6147],[-107.8367,46.5976],[-107.7971,46.5954],[-107.7986,46.495],[-107.7781,46.4951],[-107.7829,46.3948],[-107.9102,46.3931],[-107.9299,46.3935],[-107.9299,46.3779],[-107.947,46.3773],[-107.9482,46.3649],[-107.9693,46.3644],[-107.9712,46.3493],[-107.9915,46.3502],[-107.9901,46.335],[-108.0099,46.3358],[-108.0112,46.3171],[-108.0116,46.3065],[-108.0254,46.3068],[-108.0266,46.2761],[-108.0271,46.2624],[-108.0896,46.2626],[-108.1113,46.263],[-108.131,46.2628],[-108.3197,46.2632],[-108.3195,46.2504],[-108.3622,46.2502],[-108.3627,46.2351],[-108.3838,46.2354],[-108.4035,46.2352],[-108.4033,46.2196],[-108.4035,46.1949],[-108.4032,46.1812],[-108.4037,46.1665],[-108.4035,46.1528],[-108.4035,46.1326],[-108.5301,46.1327],[-108.5518,46.133],[-108.6574,46.1331],[-108.7782,46.1328],[-108.7778,46.2762],[-108.7988,46.2765],[-108.7993,46.3072],[-108.8211,46.307],[-108.822,46.3216],[-108.8325,46.3222],[-108.8318,46.3511],[-108.8423,46.3517],[-108.8419,46.3668],[-108.8636,46.3666],[-108.8634,46.3781],[-108.8642,46.4509],[-108.8846,46.4525],[-108.8856,46.4915],[-108.9074,46.4918],[-108.906,46.5775],[-108.9912,46.5775],[-108.9902,46.622],[-109.0101,46.6209],[-109.0104,46.6649],[-109.0094,46.7378],[-109.0091,46.7516],[-108.9038,46.7504],[-108.8647,46.7504],[-108.817,46.7507],[-108.7567,46.75],[-108.7382,46.7497],[-108.6958,46.7496],[-108.6276,46.7487]]],[[[-102.9587,45.2128],[-102.958,45.1251],[-102.9581,45.0388],[-102.9576,44.7781],[-102.9589,44.69],[-102.9653,44.6898],[-102.966,44.6036],[-103.1861,44.6039],[-103.2066,44.6039],[-103.3273,44.6042],[-103.4467,44.6053],[-103.5666,44.6044],[-103.8156,44.6048],[-103.8258,44.6023],[-103.8256,44.5982],[-103.8234,44.5937],[-103.8324,44.5939],[-103.8309,44.5889],[-103.8373,44.5888],[-103.8384,44.586],[-103.8417,44.5877],[-103.8464,44.5913],[-103.8533,44.5884],[-103.8567,44.5915],[-103.8641,44.5854],[-103.8702,44.5925],[-103.884,44.5985],[-103.8883,44.5952],[-103.8934,44.5942],[-103.8973,44.595],[-103.9018,44.5954],[-103.9061,44.5916],[-103.9053,44.5889],[-103.9105,44.5892],[-103.9144,44.59],[-103.9179,44.5849],[-103.9262,44.5838],[-103.9344,44.5799],[-103.9396,44.5812],[-103.9446,44.5783],[-103.9454,44.5819],[-103.9511,44.5808],[-103.9549,44.5789],[-103.9671,44.5791],[-103.9761,44.5811],[-103.9813,44.5814],[-103.983,44.5777],[-103.9997,44.5773],[-104.0183,44.5773],[-104.0229,44.5799],[-104.035,44.5782],[-104.0399,44.574],[-104.0457,44.5734],[-104.0564,44.5717],[-104.0571,44.9818],[-104.0571,44.9987],[-104.0397,44.9986],[-104.0399,45.0602],[-104.0402,45.1563],[-104.0403,45.169],[-104.0403,45.1774],[-104.0403,45.1832],[-104.0406,45.2143],[-104.041,45.2639],[-104.0425,45.5572],[-104.0426,45.5736],[-104.0424,45.6245],[-104.0425,45.6437],[-104.0425,45.6578],[-104.0425,45.6656],[-104.0426,45.6717],[-104.0426,45.6835],[-104.0433,45.7735],[-104.0434,45.7951],[-104.0435,45.8098],[-104.0437,45.8405],[-104.0439,45.8799],[-104.0441,45.9063],[-104.0443,45.9438],[-102.9956,45.944],[-102.9425,45.944],[-102.9445,45.8189],[-102.9439,45.7311],[-102.955,45.7318],[-102.9558,45.5584],[-102.9565,45.4711],[-102.9539,45.3852],[-102.9578,45.3851],[-102.9605,45.2982],[-102.9587,45.2128]]]]},\"properties\":{\"name\":\"Musselshell\",\"state\":\"MT\"}}]}","volume":"9","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-01-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Preston, Todd M. 0000-0002-8812-9233","orcid":"https://orcid.org/0000-0002-8812-9233","contributorId":204676,"corporation":false,"usgs":true,"family":"Preston","given":"Todd","email":"","middleInitial":"M.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":862047,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Johnston, Aaron N. 0000-0003-4659-0504","orcid":"https://orcid.org/0000-0003-4659-0504","contributorId":201768,"corporation":false,"usgs":true,"family":"Johnston","given":"Aaron","email":"","middleInitial":"N.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":862048,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ebenhoch, Kyle Gregory 0000-0001-7046-5557","orcid":"https://orcid.org/0000-0001-7046-5557","contributorId":299946,"corporation":false,"usgs":true,"family":"Ebenhoch","given":"Kyle","email":"","middleInitial":"Gregory","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":862049,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Diehl, Robert H. 0000-0001-9141-1734 rhdiehl@usgs.gov","orcid":"https://orcid.org/0000-0001-9141-1734","contributorId":3396,"corporation":false,"usgs":true,"family":"Diehl","given":"Robert","email":"rhdiehl@usgs.gov","middleInitial":"H.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":862050,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70248758,"text":"70248758 - 2023 - A multimodal data fusion and deep learning framework for large-scale wildfire surface fuel mapping","interactions":[],"lastModifiedDate":"2023-09-20T11:50:04.288946","indexId":"70248758","displayToPublicDate":"2023-01-17T06:44:20","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5678,"text":"Fire","active":true,"publicationSubtype":{"id":10}},"title":"A multimodal data fusion and deep learning framework for large-scale wildfire surface fuel mapping","docAbstract":"The identification of factors that may be forcing ecological observations to approach the upper boundary provides insight into potential mechanisms affecting driver-response relationships, and can help inform ecosystem management, but has rarely been explored. In this study, we propose a novel framework integrating quantile regression with interpretable machine learning. In the first stage of the framework, we estimate the upper boundary of a driver-response relationship using quantile regression. Next, we calculate “potentials” of the response variable depending on the driver, which are defined as vertical distances from the estimated upper boundary of the relationship to observations in the driver-response variable scatter plot. Finally, we identify key factors impacting the potential using a machine learning model. We illustrate the necessary steps to implement the framework using the total phosphorus (TP)-Chlorophyll a (CHL) relationship in lakes across the continental US. We found that the nitrogen to phosphorus ratio (N:P), annual average precipitation, total nitrogen (TN), and summer average air temperature were key factors impacting the potential of CHL depending on TP. We further revealed important implications of our findings for lake eutrophication management. The important role of N:P and TN on the potential highlights the co-limitation of phosphorus and nitrogen and indicates the need for dual nutrient criteria. Future wetter and/or warmer climate scenarios can decrease the potential which may reduce the efficacy of lake eutrophication management. The novel framework advances the application of quantile regression to identify factors driving observations to approach the upper boundary of driver-response relationships.
","language":"English","publisher":"Springer","doi":"10.1007/s11783-023-1676-2","usgsCitation":"Liang, Z., Xu, Y., Zhao, G., Lu, W., Fu, Z., Wang, S., and Wagner, T., 2023, Approaching the upper boundary of driver-response relationships: Identifying factors using a novel framework integrating quantile regression with interpretable machine learning: Frontiers of Environmental Science & Engineering, v. 17, 76, https://doi.org/10.1007/s11783-023-1676-2.","productDescription":"76","ipdsId":"IP-137079","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":466007,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","noUsgsAuthors":false,"publicationDate":"2023-01-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Liang, Zhongyao","contributorId":347986,"corporation":false,"usgs":false,"family":"Liang","given":"Zhongyao","affiliations":[{"id":83275,"text":"Chinese Research Academy of Environmental Sciences","active":true,"usgs":false}],"preferred":false,"id":922791,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xu, Yaoyang","contributorId":347987,"corporation":false,"usgs":false,"family":"Xu","given":"Yaoyang","affiliations":[{"id":32415,"text":"Chinese Academy of Sciences","active":true,"usgs":false}],"preferred":false,"id":922792,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Zhao, Gang","contributorId":347988,"corporation":false,"usgs":false,"family":"Zhao","given":"Gang","affiliations":[{"id":30217,"text":"Carnegie Institution for Science","active":true,"usgs":false}],"preferred":false,"id":922793,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lu, Wentao","contributorId":347989,"corporation":false,"usgs":false,"family":"Lu","given":"Wentao","affiliations":[{"id":83276,"text":"Institute of Strategic Planning","active":true,"usgs":false}],"preferred":false,"id":922794,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fu, Zhenghui","contributorId":347990,"corporation":false,"usgs":false,"family":"Fu","given":"Zhenghui","affiliations":[{"id":83275,"text":"Chinese Research Academy of Environmental Sciences","active":true,"usgs":false}],"preferred":false,"id":922795,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Wang, Shuhang","contributorId":347991,"corporation":false,"usgs":false,"family":"Wang","given":"Shuhang","affiliations":[{"id":83275,"text":"Chinese Research Academy of Environmental Sciences","active":true,"usgs":false}],"preferred":false,"id":922796,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":922797,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70240683,"text":"70240683 - 2023 - Evaluating the spatial and temporal variability of groundwater uptake by riparian vegetation in a humid southeastern US catchment","interactions":[],"lastModifiedDate":"2023-04-12T13:48:07.481221","indexId":"70240683","displayToPublicDate":"2023-01-10T06:40:24","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1447,"text":"Ecohydrology","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating the spatial and temporal variability of groundwater uptake by riparian vegetation in a humid southeastern US catchment","docAbstract":"In environments with shallow water tables, vegetation may use groundwater to support transpiration (TG). This process has been carefully studied in some arid climates but rarely in humid climates—even those with severe droughts and seasonal water deficits. As such, the role of TG in humid-catchment hydrology is poorly constrained. We analysed water table fluctuations from nine monitoring wells along three transects in a second-order forested catchment to estimate TG at plot and whole-riparian zone scales. Average TG estimated around all well locations ranged from 1.06 to 4.95 mm d−1 and did not change systematically as a function of distance from stream channel or with plot-scale tree basal area. Counter to some previous studies, we found that TG was greater when the water table depth was deeper. Furthermore, the pattern of TG with water table depth was not monotonic at all locations. The ratio of TG to potential evapotranspiration tended to increase over the growing season, reflecting the progressive decrease in soil moisture storage and a greater reliance by vegetation on groundwater. Due to the lack of consistent spatial patterns in TG, we explored the number of monitoring wells needed to consistently estimate average TG within the 95% confidence bounds of the true mean. Based on this analysis, six or more wells were needed to consistently fall within the 95% confidence interval of the true mean. While this is based on the observed variability at a single site, it provides information for others considering this approach in similar upland forested catchments in humid regions.
Belts of Cordilleran arc plutons in the eastern part of the Mojave crustal province, inboard from the southwestern North American plate boundary, record major magmatic pulses at ca. 180–160 and 75 Ma and smaller pulses at ca. 100 and 20 Ma. This cyclic magmatism likely reflects evolving plate-margin processes. Zircon Lu-Hf isotopic characteristics and inherited zircons for different-age plutons may relate magma sources to evolving tectonics. Sources similar in age to the bulk of the exposed Mojave crust (1.6–1.8 Ga) dominated the magmas. Rare zircons having εHf(t) values as low as −52 indicate that Cretaceous melt sources also included more ancient crustal components, such as Archean-derived detritus in supracrustal gneisses of the Vishnu basin. Some rocks signal contributions from mantle lithosphere (in the Miocene) or asthenosphere (middle Cretaceous).
Temporal shifts in isotopic pattern in this sample of the Cordillera relate to cyclic pulses of magmatic flux. Hf-isotopic pull-downs suggestive of dominantly crustal sources characterize the Jurassic and Late Cretaceous flare-ups. The Late Cretaceous flare-up, occurring near the onset of flat-slab subduction, produced abundant Proterozoic xenocrystic zircon and Hf isotopes implicating derivation largely from heterogeneous deep Mojave crust. Isotopic pull-ups characterize the lower-flux middle Cretaceous and Miocene magmatic episodes. The middle Cretaceous pulse ca. 105–95 Ma produced Mojave crust signals but also the isotopically most juvenile magmatic zircons, ranging upward to barely positive εHf values and suspected to signal an asthenosphere contribution. This may point toward transtension or slab retreat causing 105–95 Ma backarc extension in the Mojave hinterland of the Cordillera. That possibility of backarc extension raises questions about the tectonic environment of the contemporaneous main Sierra Nevada high-flux arc closer to the continental margin.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02438.1","usgsCitation":"Howard, K., Shaw, S., and Allen, C.M., 2023, Magmatic record of changing Cordilleran plate-boundary conditions—Insights from Lu-Hf isotopes in the Mojave Desert: Geosphere, v. 19, no. 1, p. 1-18, https://doi.org/10.1130/GES02438.1.","productDescription":"18 p.","startPage":"1","endPage":"18","ipdsId":"IP-122981","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":444958,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02438.1","text":"Publisher Index Page"},{"id":416749,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.84712822361877,\n 35.76857570064546\n ],\n [\n -116.84712822361877,\n 34.10320204421376\n ],\n [\n -114.09074554989823,\n 34.10320204421376\n ],\n [\n -114.09074554989823,\n 35.76857570064546\n ],\n [\n -116.84712822361877,\n 35.76857570064546\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"19","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-01-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Howard, Keith A. 0000-0002-6462-2947","orcid":"https://orcid.org/0000-0002-6462-2947","contributorId":264832,"corporation":false,"usgs":true,"family":"Howard","given":"Keith A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":871675,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shaw, S.E.","contributorId":304803,"corporation":false,"usgs":false,"family":"Shaw","given":"S.E.","affiliations":[{"id":16788,"text":"Macquarie University","active":true,"usgs":false}],"preferred":false,"id":871676,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Allen, Charlotte M. 0000-0002-7288-6758","orcid":"https://orcid.org/0000-0002-7288-6758","contributorId":292917,"corporation":false,"usgs":false,"family":"Allen","given":"Charlotte","email":"","middleInitial":"M.","affiliations":[{"id":63074,"text":"Research School of Earth Sciences, The Australian National University, Canberra, ACT 0200, Australia","active":true,"usgs":false}],"preferred":false,"id":871677,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70240176,"text":"70240176 - 2023 - Mapping first to final uses for rare earth elements, globally and in the United States","interactions":[],"lastModifiedDate":"2023-03-01T17:20:43.070604","indexId":"70240176","displayToPublicDate":"2022-12-30T06:33:56","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2351,"text":"Journal of Industrial Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Mapping first to final uses for rare earth elements, globally and in the United States","docAbstract":"Estimating the material flows of rare earth elements (REEs) is essential to understanding which industries are most vulnerable to potential REE supply disruptions which, in turn, may inform policy recommendations aimed at reducing the supply risk. However, the REEs are a group of mineral commodities characterized by highly uncertain estimates of supply and demand due to the REE market's complexity, opacity, and small size. In this study, a streamlined methodology was applied to map mineral commodity first-use to final-use applications and to estimate total requirements at the national level based on available industrial data for final-use finished goods. This analysis examines REEs both as a group and individually, showing that total US requirements are between 15% and 16.5% of world requirements for the year 2015, the latest year with the most complete information available. The findings shed light on US industrial capabilities by revealing the discrepancy between the types of REEs that go into US raw material consumption and those that are contained in embedded consumption. For instance, given the United States’ large oil refining industry, US raw material consumption of lanthanum is quite high. In contrast, US raw material consumption of neodymium is relatively low, whereas embedded demand is comparatively high. This reflects the lack of industrial capacity to process REE concentrates into magnet material combined with the US's high imports of products that contain rare earth permanent magnets.