The Rangeland Condition Monitoring Assessment and Projection (RCMAP) project has partnered with the Bureau of Land Management to provide annual maps of rangeland vegetation condition across the Western United States from 1985 to present. Annual mapping can assist land managers and scientists with monitoring changes to vegetation composition, evaluating past management practices, targeting future improvements, determining locations of critical wildlife habitat, and assessing landscape health and fragmentation. Impacts of climate variability and long-term change are often gradual and frequently do not present as a land cover change (for example, shrubland to grassland); however, RCMAP fractional vegetation cover data capture these gradual changes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223036","usgsCitation":"Rigge, M.B., 2022, Rangeland Condition Monitoring Assessment and Projection (RCMAP): U.S. Geological Survey Fact Sheet 2022–3036, 2 p., https://doi.org/10.3133/fs20223036.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","ipdsId":"IP-139947","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":401389,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223036/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":401374,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3036/coverthb.jpg"},{"id":401375,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3036/fs20223036.pdf","text":"Report","size":"8.20 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3036"},{"id":401377,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3036/images"},{"id":401376,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3036/fs20223036.XML"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
Invasive species threaten island biodiversity globally. For example, the Shiny Cowbird (Molothrus bonariensis) parasitizes many of Puerto Rico’s endemic species, particularly in the open forests in the island’s southwest. Less is known, however, about cowbird parasitism in the agro-ecological highlands, which contain a patchwork of forests, shaded-coffee plantations, and coffee farms without shade. In this paper, we estimated co-occurrence rates, a potential indicator of parasitism rates, between the cowbird and four host species across these three land uses, hypothesizing that cowbirds would most likely co-occur with their hosts in shaded-coffee farms. We also hypothesized that the presence of host species would increase the probability of cowbird occurrence. To investigate these hypotheses, we developed three Bayesian hierarchical occupancy models: one where the hosts and parasite occurred independently, one that used the latent host species richness as a predictor of cowbird occurrence, and one that used each latent host occurrence state as predictors. These methods addressed observation errors and appropriately propagated error to our predictions of co-occurrence rates. We selected the best performing model using WAIC, then used it to predict co-occurrence rates. While there was some evidence that host species richness increased the probability of cowbirds, the parsimonious model assumed no interaction. With this model, we found that cowbirds were more likely to overlap with certain hosts in shaded-coffee plantations. This may suggest increased parasitism at these plantations, potentially presenting challenges for managers who advocate for shade restoration to gain ecological services such as biodiversity conservation.
","language":"English","publisher":"Springer","doi":"10.1007/s10530-022-02825-3","usgsCitation":"Patton, P.T., Pacifici, K., and Collazo, J.A., 2022, Modeling and estimating co-occurrence between the invasive Shiny Cowbird and its Puerto Rican hosts: Biological Invasions, v. 24, p. 2951-2960, https://doi.org/10.1007/s10530-022-02825-3.","productDescription":"10 p.","startPage":"2951","endPage":"2960","ipdsId":"IP-140193","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":433316,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Puerto 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0000-0002-1816-7744","orcid":"https://orcid.org/0000-0002-1816-7744","contributorId":217287,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime","email":"","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908540,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70231843,"text":"70231843 - 2022 - 2021 Tinian Island forest bird abundance estimates","interactions":[],"lastModifiedDate":"2022-05-31T13:46:34.099268","indexId":"70231843","displayToPublicDate":"2022-05-31T08:25:54","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"seriesTitle":{"id":257,"text":"Hawai‘i Cooperative Studies Unit Technical Report","active":false,"publicationSubtype":{"id":3}},"seriesNumber":"105","title":"2021 Tinian Island forest bird abundance estimates","docAbstract":"The U.S. Navy, through Micronesian Environmental Services, surveyed landbirds in the Military Lease Area on Tinian Island in May and June 2021 using point-transect distance sampling methods. There were 2,074 individuals of 14 species detected during 123 point counts. Six species were detected during >50% of the counts and were observed at relatively high abundances, while eight species occurred at <50% of the counts and were uncommon to rare. Densities of native landbirds in the Military Lease Area ranged from the uncommon Mariana kingfisher (Todiramphus albicilla) at 0.46 birds/ha (95% confidence interval [CI] = 0.33–0.63) to the very abundant bridled white-eye (Zosterops conspicillatus) at 102.63 birds/ha (95%CI = 86.70–122.91). Most distances recorded during the 2021 Military Lease Area survey were rounded to distance intervals of 0 and 5. Measuring exact distances of detected animals is preferable to collecting distances grouped into bins or rounding. Direct comparison with previously published estimates was not possible because of changes in the sampling frame; however, densities of six species were greater, two were smaller, and one was similar to the 2008 survey estimates for the Hagoi, Diablo, and Masalog regions. Our findings indicate that the landbird community in the Military Lease Area appears to be dynamic and resilient.
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The level of detail presented in LANDFIRE’s classifications of disturbance, vegetation, and fuels is unparalleled and can be used in a variety of applications, including (1) modeling wildfire risk and fire behavior, (2) modeling habitat and species ranges, (3) understanding how disturbances affect the landscape, and (4) researching departure from precolonial conditions. Additionally, the all-lands paradigm of LANDFIRE mapping creates spatial data that do not stop at jurisdictional boundaries. The primary research and management applications of LANDFIRE data are detailed in this fact sheet, providing users with a well-rounded understanding of the potential of LANDFIRE’s spatial data layers.
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Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
No abstract available.
","language":"English","publisher":"U.S. Fish and Wildlife Service","collaboration":"Great Lakes Fishery Commission (GLFC); Great Lakes Restoration Initiative (GLRI); Invasive Carp Regional Coordinating Committee (ICRCC); U.S. Army Corps of Engineers (USACE); U.S. Fish and Wildlife Service (USFWS); Kentucky Department of Fish and Wildlife Resources; University of MN; and Fish Guidance Systems","usgsCitation":"Simmonds, R., Knights, B.C., Fritts, A.K., Stanton, J.C., Brey, M.K., and Vallazza, J.M., 2022, Lake Barkley BioAcoustic fish fence effectiveness study project status update, 1 p.","productDescription":"1 p.","ipdsId":"IP-136972","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":431300,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":431290,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://controlinvasivecarpmn.com/wp-content/uploads/2022/02/lake-barkley-bio-acoustic-fish-fence-Jan-2022-pahmplet-1-1.pdf"}],"country":"United States","state":"Kentucky","otherGeospatial":"Lake Barkley, Barkley Lock and Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.3011119114417,\n 37.03353323113609\n ],\n [\n -88.3011119114417,\n 36.99818111699841\n ],\n [\n -88.24551735688884,\n 36.99818111699841\n ],\n [\n -88.24551735688884,\n 37.03353323113609\n ],\n [\n -88.3011119114417,\n 37.03353323113609\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Simmonds, Rob","contributorId":317890,"corporation":false,"usgs":false,"family":"Simmonds","given":"Rob","email":"","affiliations":[{"id":68344,"text":"U.S. Fish and Wildlife Service (USFWS)","active":true,"usgs":false}],"preferred":false,"id":906685,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Knights, Brent C. 0000-0001-8526-8468 bknights@usgs.gov","orcid":"https://orcid.org/0000-0001-8526-8468","contributorId":2906,"corporation":false,"usgs":true,"family":"Knights","given":"Brent","email":"bknights@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":906686,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fritts, Andrea K. 0000-0003-2142-3339","orcid":"https://orcid.org/0000-0003-2142-3339","contributorId":204594,"corporation":false,"usgs":true,"family":"Fritts","given":"Andrea","email":"","middleInitial":"K.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":906687,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stanton, Jessica C. 0000-0002-6225-3703 jcstanton@usgs.gov","orcid":"https://orcid.org/0000-0002-6225-3703","contributorId":5634,"corporation":false,"usgs":true,"family":"Stanton","given":"Jessica","email":"jcstanton@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":906688,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brey, Marybeth K. 0000-0003-4403-9655 mbrey@usgs.gov","orcid":"https://orcid.org/0000-0003-4403-9655","contributorId":187651,"corporation":false,"usgs":true,"family":"Brey","given":"Marybeth","email":"mbrey@usgs.gov","middleInitial":"K.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":906689,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vallazza, Jonathan M. 0000-0003-2367-4887 jvallazza@usgs.gov","orcid":"https://orcid.org/0000-0003-2367-4887","contributorId":149362,"corporation":false,"usgs":true,"family":"Vallazza","given":"Jonathan","email":"jvallazza@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":906690,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70237968,"text":"70237968 - 2022 - River Metabolism Estimation Tools (RiverMET) with demo in the Illinois River Basin","interactions":[],"lastModifiedDate":"2022-11-02T11:49:35.521099","indexId":"70237968","displayToPublicDate":"2022-05-31T06:47:09","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12802,"text":"ESSOAr","active":true,"publicationSubtype":{"id":10}},"title":"River Metabolism Estimation Tools (RiverMET) with demo in the Illinois River Basin","docAbstract":"Ecosystem metabolism quantifies the rate of production, maintenance, and decay of organic matter in terrestrial and aquatic systems. It is a fundamental measure of energy flow associated with biomass production by photosynthesizing organisms and biomass oxidation by respiring plants, animals, algae, and bacteria (Bernhardt et al., 2022) . Ecosystem metabolism also provides an understanding of energy flow to higher trophic levels that supports secondary and tertiary productivity, as well as helping to explain when aquatic ecosystems undergo out-of-balance behaviors such as harmful algal blooms and hypoxia. Recent advances in sensor technology and modeling capabilities have enabled estimation of aquatic system metabolism and gas exchange over long time periods in rivers, streams, ponds, and wetlands where oxygen sensors have been deployed. Here we present RiverMET, a framework for estimation of river metabolism, with workflows to streamline data preparation, run a stream metabolism model, assess the model performance, and flag and censor final output data. The workflows are specifically tailored to use streamMetabolizer, a model for one-station calculations of stream metabolism that calculates gross primary productivity (GPP), ecosystem respiration (ER) and the air-water gas exchange rate constant (K600). We advise potential users of RiverMET to review core publications for the streamMetabolizer model (Appling et al., 2018 a, b, c) to ensure best practices that produce the most useful results. We encourage feedback about our workflows, although issues regarding the streamMetabolizer model itself should be referred to the model authors. We tested RiverMET by calculating GPP, ER, and K600 across 17 river sites in the Illinois River basin (ILRB). Each river had between one and nine years of sensor data appropriate for modeling metabolism. In total, metabolism was modeled on 15,176 days between 2005 and 2020. Overall confidence in the results was rated as high at nine river sites, medium at six river sites, and poor at two river sites. Twenty-nine percent of the total modeled days had performance metrics that triggered flags. Metrics used for daily flagging are provided with the final output, with an option to only retain the censored daily outputs with high confidence (representing 72 %, i.e., 10,938 days, of the total days modeled). This work was completed as part of the U.S. Geological Survey Proxies Project, an effort supported by the Water Mission Area (WMA) Water Quality Processes program to develop estimation methods for harmful algal blooms (HABs), per- and polyfluoroalkyl substances (PFAS), and metals, at multiple spatial and temporal scales.
","language":"English","publisher":"Earth and Space Science Open Archive","doi":"10.1002/essoar.10511255.1","usgsCitation":"Choi, J., Quion, K.M., Reed, A., and Harvey, J., 2022, River Metabolism Estimation Tools (RiverMET) with demo in the Illinois River Basin: ESSOAr, 22 p., https://doi.org/10.1002/essoar.10511255.1.","productDescription":"22 p.","ipdsId":"IP-139945","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":435833,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TEBOUR","text":"USGS data release","linkHelpText":"RiverMET: Workflow and scripts for river metabolism estimation including Illinois River Basin application, 2005 - 2020"},{"id":409056,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Illinois River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -86.901423683579,\n 42.70071815175049\n ],\n [\n -91.86724399607925,\n 42.70071815175049\n ],\n [\n -91.86724399607925,\n 39.14935275277796\n ],\n [\n -86.901423683579,\n 39.14935275277796\n ],\n [\n -86.901423683579,\n 42.70071815175049\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Choi, Jay 0000-0003-1276-481X jchoi@usgs.gov","orcid":"https://orcid.org/0000-0003-1276-481X","contributorId":219096,"corporation":false,"usgs":true,"family":"Choi","given":"Jay","email":"jchoi@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":856403,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Quion, Katherine Michelle Bernabe 0000-0003-2388-7508","orcid":"https://orcid.org/0000-0003-2388-7508","contributorId":298787,"corporation":false,"usgs":true,"family":"Quion","given":"Katherine","email":"","middleInitial":"Michelle Bernabe","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":856404,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reed, Ariel 0000-0002-0792-5204","orcid":"https://orcid.org/0000-0002-0792-5204","contributorId":298788,"corporation":false,"usgs":false,"family":"Reed","given":"Ariel","affiliations":[],"preferred":false,"id":856405,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harvey, Judson 0000-0002-2654-9873","orcid":"https://orcid.org/0000-0002-2654-9873","contributorId":219104,"corporation":false,"usgs":true,"family":"Harvey","given":"Judson","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":856406,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70239791,"text":"70239791 - 2022 - Hydrologic controls on peat permafrost and carbon processes: New insights from past and future modeling","interactions":[],"lastModifiedDate":"2023-01-20T12:46:34.734013","indexId":"70239791","displayToPublicDate":"2022-05-31T06:44:44","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5738,"text":"Frontiers in Environmental Science","active":true,"publicationSubtype":{"id":10}},"title":"Hydrologic controls on peat permafrost and carbon processes: New insights from past and future modeling","docAbstract":"Soil carbon (C) in permafrost peatlands is vulnerable to decomposition with thaw under a warming climate. The amount and form of C loss likely depends on the site hydrology following permafrost thaw, but antecedent conditions during peat accumulation are also likely important. We test the role of differing hydrologic conditions on rates of peat accumulation, permafrost formation, and response to warming at an Arctic tundra fen using a process-based model of peatland dynamics in wet and dry landscape settings that persist from peat initiation in the mid-Holocene through future simulations to 2100 CE and 2300 CE. Climate conditions for both the wet and dry landscape settings are driven by the same downscaled TraCE-21ka transient paleoclimate simulations and CCSM4 RCP8.5 climate drivers. The landscape setting controlled the rates of peat accumulation, permafrost formation and the response to climatic warming and permafrost thaw. The dry landscape scenario had high rates of initial peat accumulation (11.7 ± 3.4 mm decade−1) and rapid permafrost aggradation but similar total C stocks as the wet landscape scenario. The wet landscape scenario was more resilient to 21st century warming temperatures than the dry landscape scenario and showed 60% smaller C losses and 70% more new net peat C additions by 2100 CE. Differences in the modeled responses indicate the largest effect is related to the landscape setting and basin hydrology due to permafrost controls on decomposition, suggesting an important sensitivity to changing runoff patterns. These subtle hydrological effects will be difficult to capture at circumpolar scales but are important for the carbon balance of permafrost peatlands under future climate warming.
Sea level rise (SLR) and saltwater intrusion are driving inland shifts in coastal ecosystems. Here, we make high-resolution (1 m) predictions of land conversion under future SLR scenarios in 81 watersheds surrounding Chesapeake Bay, United States, a hotspot for accelerated SLR and saltwater intrusion. We find that 1050–3748 km2 of marsh could be created by 2100, largely at the expense of forested wetlands. Predicted marsh migration exceeds total current tidal marsh area and is ~ 4× greater than historical observations. Anthropogenic land use in marsh migration areas is concentrated within a few watersheds and minimally impacts calculated metrics of marsh resilience. Despite regional marsh area maintenance, local ecosystem service replacement within vulnerable watersheds remains uncertain. However, our work suggests that topography rather than land use drives spatial variability in wetland vulnerability regionally, and that rural land conversion is needed to compensate for extensive areal losses on heavily developed coasts globally.
The objective of this work is to characterize the geochemistry of a suite of natural gas samples from five fields in order to improve the understanding of the lacustrine petroleum system of the pre-salt section from the Santos Basin (Brazil). Additionally, the distribution of volatile organic sulfur compounds (VOSC) in petroleum reservoirs was examined to investigate possible applications to petroleum systems assessments. The hydrocarbon gases were generated by thermogenic processes associated with the oil window. The 13C-enriched values for C1 (>−40‰) were interpreted as an organic source signature rather than an indication of thermal maturity, except for the oil occurrence (Field B), where a different fluid charge mainly composed of methane and CO2 from a minor external kitchen area in the Santos Basin was identified. The molecular composition and the carbon and hydrogen isotopic data of the hydrocarbon gases, when combined with the VOSC molecular compositional data, allow the identification of four gas families associated with different kitchens and/or migrations pathways. The total VOSC concentrations range from 0.7 to 23.9 ppm by volume (ppmV). The organic sulfides are mainly composed of carbonyl sulfide (COS) and diethyl sulfide. The main thiol compound is ethanethiol. The cyclic VOSC are primarily composed of thiophene, with a negligible amount of branched thiophenes (<0.1 ppmV). H2S showed a strong positive Pearson's correlation with COS and methanethiol (MeSH) concentrations (r = 0.943 and 0.807, respectively). This suggests that COS and MeSH formation was linked to H2S generation and/or post-catagenetic interactions between hydrocarbons and H2S, mainly related to thermochemical sulfate reduction (TSR). In contrast, the distribution of higher molecular weight VOSC seems to be controlled by source rock facies, rather than H2S concentration. Principal component analysis of the VOSC compositional data identified some subgroups within the gas families mainly associated with TSR. The results presented in this work reveal that VOSC can be an important auxiliary tool in petroleum system studies.
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P.","contributorId":292657,"corporation":false,"usgs":false,"family":"Guzzo","given":"Jarbas","email":"","middleInitial":"V. P.","affiliations":[{"id":62961,"text":"Petrobras","active":true,"usgs":false}],"preferred":false,"id":845412,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Diaz, Rut A.","contributorId":292658,"corporation":false,"usgs":false,"family":"Diaz","given":"Rut","email":"","middleInitial":"A.","affiliations":[{"id":62963,"text":"Fluminense Federal University, Brazil","active":true,"usgs":false}],"preferred":false,"id":845413,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Albuquerque, Ana Luiza S.","contributorId":292659,"corporation":false,"usgs":false,"family":"Albuquerque","given":"Ana","email":"","middleInitial":"Luiza S.","affiliations":[{"id":62963,"text":"Fluminense Federal University, Brazil","active":true,"usgs":false}],"preferred":false,"id":845414,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Amrani, Alon","contributorId":225213,"corporation":false,"usgs":false,"family":"Amrani","given":"Alon","affiliations":[{"id":41077,"text":"Research Center","active":true,"usgs":false}],"preferred":false,"id":845415,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70231840,"text":"70231840 - 2022 - Understanding the water resources of a mountain-block aquifer: Tucson Mountains, Arizona","interactions":[],"lastModifiedDate":"2022-05-30T20:50:00.823956","indexId":"70231840","displayToPublicDate":"2022-05-30T15:42:31","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10769,"text":"Journal of Contemporary Water Research & Education","active":true,"publicationSubtype":{"id":10}},"title":"Understanding the water resources of a mountain-block aquifer: Tucson Mountains, Arizona","docAbstract":"Water resources are limited in arid locations such as Tucson Basin. Residential development in the Tucson Mountains to the west of Tucson, Arizona, is limited by groundwater resources. Groundwater samples were collected from fractured bedrock and alluvial aquifers surrounding the Tucson Mountains to assess water quality and recharge history through measurement of stable O, H, and S isotopes; tritium; and 14C. Most groundwater is a mixture of different ages but is commonly several thousand years old. A few sampling locations indicated a component of water recharged after the above-ground nuclear testing of the mid 1950s, and these sites may represent locations near where the aquifer receives present-day recharge. The Tucson Mountains also host sulfide deposits associated with fractures and replacement zones; these locally contribute to poor-quality groundwater. Projections of future climate predict intensifying drought in southwestern North America. In the study area, a combination of strategies such as rainwater harvesting, exploitation of renewable water, and low groundwater use could be used for sustainable use of the groundwater supply.
","language":"English","publisher":"Wiley","doi":"10.1111/j.1936-704X.2021.3369.x","usgsCitation":"Eastoe, C.J., and Beisner, K.R., 2022, Understanding the water resources of a mountain-block aquifer: Tucson Mountains, Arizona: Journal of Contemporary Water Research & Education, v. 175, no. 1, https://doi.org/10.1111/j.1936-704X.2021.3369.x.","productDescription":"14 p.","startPage":"1-14","ipdsId":"IP-130604","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":447613,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/j.1936-704x.2021.3369.x","text":"Publisher Index Page"},{"id":401364,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Tucson Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.27296447753905,\n 32.155268542097815\n ],\n [\n -110.99761962890625,\n 32.155268542097815\n ],\n [\n -110.99761962890625,\n 32.377062004744786\n ],\n [\n -111.27296447753905,\n 32.377062004744786\n ],\n [\n -111.27296447753905,\n 32.155268542097815\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"175","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-05-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Eastoe, Christopher J.","contributorId":173510,"corporation":false,"usgs":false,"family":"Eastoe","given":"Christopher","email":"","middleInitial":"J.","affiliations":[{"id":6624,"text":"University of Arizona, Laboratory of Tree-Ring Research","active":true,"usgs":false}],"preferred":false,"id":843936,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beisner, Kimberly R. 0000-0002-2077-6899 kbeisner@usgs.gov","orcid":"https://orcid.org/0000-0002-2077-6899","contributorId":2733,"corporation":false,"usgs":true,"family":"Beisner","given":"Kimberly","email":"kbeisner@usgs.gov","middleInitial":"R.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":843937,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70231823,"text":"70231823 - 2022 - Impoundment increases methane emissions in Phragmites-invaded coastal wetlands ","interactions":[],"lastModifiedDate":"2022-07-08T13:36:32.552842","indexId":"70231823","displayToPublicDate":"2022-05-30T15:24:26","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Impoundment increases methane emissions in Phragmites-invaded coastal wetlands ","title":"Impoundment increases methane emissions in Phragmites-invaded coastal wetlands ","docAbstract":"Saline tidal wetlands are important sites of carbon sequestration and produce negligible methane (CH4) emissions due to regular inundation with sulfate-rich seawater. Yet, widespread management of coastal hydrology has restricted tidal exchange in vast areas of coastal wetlands. These ecosystems often undergo impoundment and freshening, which in turn cause vegetation shifts like invasion by Phragmites, that affect ecosystem carbon balance. Understanding controls and scaling of carbon exchange in these understudied ecosystems is critical for informing climate consequences of blue carbon restoration and/or management interventions. Here, we (1) examine how carbon fluxes vary across a salinity gradient (4–25 psu) in impounded and natural, tidally unrestricted Phragmites wetlands using static chambers and (2) probe drivers of carbon fluxes within an impounded coastal wetland using eddy covariance at the Herring River in Wellfleet, MA, United States. Freshening across the salinity gradient led to a 50-fold increase in CH4 emissions, but effects on carbon dioxide (CO2) were less pronounced with uptake generally enhanced in the fresher, impounded sites. The impounded wetland experienced little variation in water-table depth or salinity during the growing season and was a strong CO2 sink of −352 g CO2-C m−2 year−1 offset by CH4 emission of 11.4 g CH4-C m−2 year−1. Growing season CH4 flux was driven primarily by temperature. Methane flux exhibited a diurnal cycle with a night-time minimum that was not reflected in opaque chamber measurements. Therefore, we suggest accounting for the diurnal cycle of CH4 in Phragmites, for example by applying a scaling factor developed here of ~0.6 to mid-day chamber measurements. Taken together, these results suggest that although freshened, impounded wetlands can be strong carbon sinks, enhanced CH4 emission with freshening reduces net radiative balance. Restoration of tidal flow to impounded ecosystems could limit CH4 production and enhance their climate regulating benefits.
1. Ecological restoration and conservation efforts are increasing worldwide and the management of intraspecific genetic variation in plants and animals, an important component of biodiversity, is increasingly valued. As a result, tailorable, spatially explicit approaches to map genetic variation are needed to support decision-making and management frameworks related to the recovery of threatened and endangered species and the maintenance of genetic resources in species utilized by humans, such as for restoration or agricultural purposes.
2. Here, we describe and demonstrate a workflow to spatially interpolate patterns of genetic differentiation using novel functions in the R package POPMAPS (Population Management using Ancestry Probability Surfaces). Our approach uses empirical genetic data to estimate ancestry coefficients across a user-defined landscape correlated with patterns of differentiation in the focal species. The resulting surface, which we term the ancestry probability surface, includes two components: hard population boundaries and estimations of uncertainty that represent confidence in population assignments (i.e., ancestry probabilities).
3. An ancestry probability surface developed for Hilaria jamesii, an important graminoid utilized in restoration across the western United States, demonstrates the functionality of POPMAPS. Genetic distances among empirical sites correlated better with least-cost distances across suitable habitat than with geographic distances, informing the surface over which the interpolation was conducted (i.e., a model indicating habitat suitability). A jackknifing procedure identified parameter values resulting in robust population assignments across the species’ range, which were utilized in downstream analyses to estimate ancestry coefficients from empirical data. Ancestry coefficients were translated into ancestry probabilities, which tended to be low for cells that were intermediate in distance between empirical sampling locations representing different populations or when influenced by empirical sampling locations with mixed genetic ancestry.
4. POPMAPS allows users to tailor parameter values and analytical approaches and thereby incorporate species-specific biological characteristics and desired levels of uncertainty into maps illustrating patterns of genetic differentiation. Ancestry probability surfaces may be used to guide management or investigate further ecological or evolutionary hypotheses. We discuss how maps produced by POPMAPS can inform multiple management challenges including species recovery planning and the utilization of commonly used species in restoration.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/2041-210X.13902","usgsCitation":"Massatti, R., and Winkler, D.E., 2022, Spatially explicit management of genetic diversity using ancestry probability surfaces: Methods in Ecology and Evolution, v. 13, no. 12, p. 2668-2681, https://doi.org/10.1111/2041-210X.13902.","productDescription":"14 p.","startPage":"2668","endPage":"2681","ipdsId":"IP-133238","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":447618,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/2041-210x.13902","text":"Publisher Index Page"},{"id":435837,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96VLOA5","text":"USGS data release","linkHelpText":"POPMAPS: An R package to estimate ancestry probability surfaces"},{"id":401362,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"12","noUsgsAuthors":false,"publicationDate":"2022-06-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Massatti, Robert 0000-0001-5854-5597","orcid":"https://orcid.org/0000-0001-5854-5597","contributorId":207294,"corporation":false,"usgs":true,"family":"Massatti","given":"Robert","email":"","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":843923,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Winkler, Daniel E. 0000-0003-4825-9073","orcid":"https://orcid.org/0000-0003-4825-9073","contributorId":206786,"corporation":false,"usgs":true,"family":"Winkler","given":"Daniel","email":"","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":843924,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70274304,"text":"70274304 - 2022 - Trans-crustal structural control of CO2-rich extensional magmatic systems revealed at Mount Erebus Antarctica","interactions":[],"lastModifiedDate":"2026-03-26T16:59:44.858337","indexId":"70274304","displayToPublicDate":"2022-05-30T11:52:03","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2842,"text":"Nature Communications","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Trans-crustal structural control of CO2-rich extensional magmatic systems revealed at Mount Erebus Antarctica","title":"Trans-crustal structural control of CO2-rich extensional magmatic systems revealed at Mount Erebus Antarctica","docAbstract":"Erebus volcano, Antarctica, with its persistent phonolite lava lake, is a classic example of an evolved, CO2-rich rift volcano. Seismic studies provide limited images of the magmatic system. Here we show using magnetotelluric data that a steep, melt-related conduit of low electrical resistivity originating in the upper mantle undergoes pronounced lateral re-orientation in the deep crust before reaching shallower magmatic storage and the summit lava lake. The lateral turn represents a structural fault-valve controlling episodic flow of magma and CO2 vapour, which replenish and heat the high level phonolite differentiation zone. This magmatic valve lies within an inferred, east-west structural trend forming part of an accommodation zone across the southern termination of the Terror Rift, providing a dilatant magma pathway. Unlike H2O-rich subduction arc volcanoes, CO2-dominated Erebus geophysically shows continuous magmatic structure to shallow crustal depths of < 1 km, as the melt does not experience decompression-related volatile supersaturation and viscous stalling.
","language":"English","publisher":"Nature","doi":"10.1038/s41467-022-30627-7","usgsCitation":"Hill, G.J., Wannamaker, P.E., Maris, V., Stodt, J.A., Kordy, M., Unsworth, M.J., Bedrosian, P.A., Wallin, E.L., Uhlmann, D.F., Ogawa, Y., and Kyle, P.R., 2022, Trans-crustal structural control of CO2-rich extensional magmatic systems revealed at Mount Erebus Antarctica: Nature Communications, v. 13, 2989, 10 p., https://doi.org/10.1038/s41467-022-30627-7.","productDescription":"2989, 10 p.","ipdsId":"IP-138531","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":501614,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41467-022-30627-7","text":"Publisher Index Page"},{"id":501590,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Antarctica, Mount Erebus","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 165,\n -78\n ],\n [\n 170,\n -78\n ],\n [\n 170,\n -77\n ],\n [\n 165,\n -77\n ],\n [\n 165,\n -78\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","noUsgsAuthors":false,"publicationDate":"2022-05-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Hill, Graham J","contributorId":367839,"corporation":false,"usgs":false,"family":"Hill","given":"Graham","middleInitial":"J","affiliations":[{"id":79730,"text":"Czech Academy of Science","active":true,"usgs":false}],"preferred":false,"id":957801,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wannamaker, Phil E","contributorId":367840,"corporation":false,"usgs":false,"family":"Wannamaker","given":"Phil","middleInitial":"E","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":957802,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Maris, Virginie","contributorId":194006,"corporation":false,"usgs":false,"family":"Maris","given":"Virginie","affiliations":[],"preferred":false,"id":957803,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stodt, J. A.","contributorId":367843,"corporation":false,"usgs":false,"family":"Stodt","given":"J.","middleInitial":"A.","affiliations":[{"id":87627,"text":"Numerical Resources LLC","active":true,"usgs":false}],"preferred":false,"id":957804,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kordy, Michael","contributorId":367844,"corporation":false,"usgs":false,"family":"Kordy","given":"Michael","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":957805,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Unsworth, Martyn J.","contributorId":367845,"corporation":false,"usgs":false,"family":"Unsworth","given":"Martyn","middleInitial":"J.","affiliations":[{"id":36696,"text":"University of Alberta","active":true,"usgs":false}],"preferred":false,"id":957806,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bedrosian, Paul A. 0000-0002-6786-1038 pbedrosian@usgs.gov","orcid":"https://orcid.org/0000-0002-6786-1038","contributorId":839,"corporation":false,"usgs":true,"family":"Bedrosian","given":"Paul","email":"pbedrosian@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":957807,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wallin, Erin L.","contributorId":367846,"corporation":false,"usgs":false,"family":"Wallin","given":"Erin","middleInitial":"L.","affiliations":[{"id":47560,"text":"University of Hawaii Manoa","active":true,"usgs":false}],"preferred":false,"id":957808,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Uhlmann, Danny F.","contributorId":367847,"corporation":false,"usgs":false,"family":"Uhlmann","given":"Danny","middleInitial":"F.","affiliations":[{"id":35541,"text":"University of Lausanne","active":true,"usgs":false}],"preferred":false,"id":957809,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ogawa, Yasuo","contributorId":302663,"corporation":false,"usgs":false,"family":"Ogawa","given":"Yasuo","email":"","affiliations":[{"id":38251,"text":"Tokyo Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":957810,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kyle, Philip R.","contributorId":174414,"corporation":false,"usgs":false,"family":"Kyle","given":"Philip","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":957811,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70256653,"text":"70256653 - 2022 - Integrated animal movement and spatial capture–recapture models: Simulation, implementation, and inference","interactions":[],"lastModifiedDate":"2024-08-29T15:02:58.967417","indexId":"70256653","displayToPublicDate":"2022-05-30T09:59:16","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Integrated animal movement and spatial capture–recapture models: Simulation, implementation, and inference","docAbstract":"Over the last decade, spatial capture–recapture (SCR) models have become widespread for estimating demographic parameters in ecological studies. However, the underlying assumptions about animal movement and space use are often not realistic. This is a missed opportunity because interesting ecological questions related to animal space use, habitat selection, and behavior cannot be addressed with most SCR models, despite the fact that the data collected in SCR studies — individual animals observed at specific locations and times — can provide a rich source of information about these processes and how they relate to demographic rates. We developed SCR models that integrated more complex movement processes that are typically inferred from telemetry data, including a simple random walk, correlated random walk (i.e., short-term directional persistence), and habitat-driven Langevin diffusion. We demonstrated how to formulate, simulate from, and fit these models with standard SCR data using data-augmented Bayesian analysis methods. We evaluated their performance through a simulation study, in which we varied the detection, movement, and resource selection parameters. We also examined different numbers of sampling occasions and assessed performance gains when including auxiliary location data collected from telemetered individuals. Across all scenarios, the integrated SCR movement models performed well in terms of abundance, detection, and movement parameter estimation. We found little difference in bias for the simple random walk model when reducing the number of sampling occasions from T = 25 to T = 15. We found some bias in movement parameter estimates under several of the correlated random walk scenarios, but incorporating auxiliary location data improved parameter estimates and significantly improved mixing during model fitting. The Langevin movement model was able to recover resource selection parameters from standard SCR data, which is particularly appealing because it explicitly links the individual-level movement process with habitat selection and population density. We focused on closed population models, but the movement models developed here can be extended to open SCR models. The movement process models could also be easily extended to accommodate additional “building blocks” of random walks, such as central tendency (e.g., territoriality) or multiple movement behavior states, thereby providing a flexible and coherent framework for linking animal movement behavior to population dynamics, density, and distribution.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.3771","usgsCitation":"Gardner, B., McClintock, B., Converse, S.J., and Hostetter, N.J., 2022, Integrated animal movement and spatial capture–recapture models: Simulation, implementation, and inference: Ecology, v. 103, e3771, 13 p., https://doi.org/10.1002/ecy.3771.","productDescription":"e3771, 13 p.","ipdsId":"IP-130421","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":447622,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"text":"Publisher Index Page"},{"id":433312,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"103","noUsgsAuthors":false,"publicationDate":"2022-07-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Gardner, B.","contributorId":341497,"corporation":false,"usgs":false,"family":"Gardner","given":"B.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":908507,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McClintock, B.T.","contributorId":341498,"corporation":false,"usgs":false,"family":"McClintock","given":"B.T.","affiliations":[{"id":38436,"text":"National Oceanic and Atmospheric Administration","active":true,"usgs":false}],"preferred":false,"id":908508,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":908509,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hostetter, Nathan J. 0000-0001-6075-2157 nhostetter@usgs.gov","orcid":"https://orcid.org/0000-0001-6075-2157","contributorId":198843,"corporation":false,"usgs":true,"family":"Hostetter","given":"Nathan","email":"nhostetter@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":908510,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70236349,"text":"70236349 - 2022 - P- and S-wave velocity estimation by ensemble Kalman inversion of dispersion data for strong motion stations in California","interactions":[],"lastModifiedDate":"2022-09-02T14:09:03.829255","indexId":"70236349","displayToPublicDate":"2022-05-30T09:01:38","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1803,"text":"Geophysical Journal International","active":true,"publicationSubtype":{"id":10}},"title":"P- and S-wave velocity estimation by ensemble Kalman inversion of dispersion data for strong motion stations in California","docAbstract":"This study uses an ensemble Kalman method for near-surface seismic site characterization of 154 network earthquake monitoring stations in California to improve the resolution of S-wave velocity (VS) and P-wave velocity (VP) profiles—up to the resolution depth—coupled with better quantification of uncertainties compared to previous site characterization studies at this network. These stations were part of the Yong et al. site characterization project, with selected stations based on future recordings of ground motions that are expected to exceed 10 per cent peak ground acceleration in 50 yr. To estimate VS and VP from experimental dispersion data, Yong et al. investigated these stations using linearized (local search and iteration) routines, and Yong et al. later studied a subset of these stations using nonlinear (global search and optimization) routines. In both studies, the selection of model parameters—that is, discretization of the VS and VP profiles with only five fixed thickness layers—was mainly based on trial and error. In contrast, this paper uses an approximate Bayesian method to assimilate experimental dispersion data and sequentially update an ensemble of particle estimates that span the VS and VP parameter spaces. Doing so, we systematically determine the most probable profiles conditioned on the experimental dispersion data, the introduced noise levels, and a priori knowledge in the form of physical constraints. We consider two configurations to discretize the soil depth from the surface to half of the maximum discernible wavelength obtained from the experimental dispersion data, namely refined and coarse models, and two initial models for each configuration to study solution multiplicity. Our results suggest that using the refined model for the top surface layers improves the resolution of near-surface site characteristics and the model’s success rate in capturing dispersion data at high frequencies. All models result in similar VS but distinct VP profiles, with increasing uncertainty at deeper layers, suggesting that the fundamental mode of Rayleigh wave dispersion data is not adequate to constrain the P-wave velocity profile and the S-wave velocity close to the resolution depth.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggac201","usgsCitation":"Bas, E.E., Seylabi, E., Yong, A., Tehrani, H., and Asimaki, D., 2022, P- and S-wave velocity estimation by ensemble Kalman inversion of dispersion data for strong motion stations in California: Geophysical Journal International, v. 231, no. 1, p. 536-551, https://doi.org/10.1093/gji/ggac201.","productDescription":"16 p.","startPage":"536","endPage":"551","ipdsId":"IP-132480","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":447625,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://resolver.caltech.edu/CaltechAUTHORS:20220804-250008000","text":"External Repository"},{"id":406134,"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 \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.58618164062499,\n 38.75408327579141\n ],\n [\n -122.684326171875,\n 37.900865092570065\n ],\n [\n -122.3876953125,\n 36.98500309285596\n ],\n [\n -121.46484375,\n 35.6126508187567\n ],\n [\n -120.4541015625,\n 34.32529192442733\n ],\n [\n -120.14648437499999,\n 33.60546961227188\n ],\n [\n -119.05883789062501,\n 32.8334428466495\n ],\n [\n -117.18017578125,\n 32.565333160841035\n ],\n [\n -114.47753906249999,\n 32.7503226078097\n ],\n [\n -114.42260742187499,\n 32.99023555965106\n ],\n [\n -114.6533203125,\n 33.073130945006625\n ],\n [\n -114.70825195312501,\n 33.38558626887102\n ],\n [\n -114.47753906249999,\n 33.63291573870479\n ],\n [\n -114.49951171875,\n 33.925129700072\n ],\n [\n -114.071044921875,\n 34.32529192442733\n ],\n [\n -114.444580078125,\n 34.67839374011646\n ],\n [\n -114.664306640625,\n 35.03899204678081\n ],\n [\n -116.46606445312499,\n 36.465471886798134\n ],\n [\n -119.58618164062499,\n 38.75408327579141\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"231","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-05-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Bas, Elif Ecem","contributorId":296121,"corporation":false,"usgs":false,"family":"Bas","given":"Elif","email":"","middleInitial":"Ecem","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":850703,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Seylabi, Elnaz","contributorId":296122,"corporation":false,"usgs":false,"family":"Seylabi","given":"Elnaz","email":"","affiliations":[{"id":16686,"text":"University of Nevada, Reno","active":true,"usgs":false}],"preferred":false,"id":850704,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yong, Alan K. 0000-0003-1807-5847","orcid":"https://orcid.org/0000-0003-1807-5847","contributorId":296123,"corporation":false,"usgs":true,"family":"Yong","given":"Alan K.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":850706,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tehrani, Hesam","contributorId":296124,"corporation":false,"usgs":false,"family":"Tehrani","given":"Hesam","email":"","affiliations":[],"preferred":false,"id":850707,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Asimaki, Domniki","contributorId":146598,"corporation":false,"usgs":false,"family":"Asimaki","given":"Domniki","email":"","affiliations":[{"id":7218,"text":"California Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":850705,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70238968,"text":"70238968 - 2022 - Stream size, temperature, and density explain body sizes of freshwater salmonids across a range of climate conditions","interactions":[],"lastModifiedDate":"2022-12-19T14:57:53.446229","indexId":"70238968","displayToPublicDate":"2022-05-30T08:57:30","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1169,"text":"Canadian Journal of Fisheries and Aquatic Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Stream size, temperature, and density explain body sizes of freshwater salmonids across a range of climate conditions","docAbstract":"Climate change and anthropogenic activities are altering the body sizes of fishes, yet our understanding of factors influencing body size for many taxa remains incomplete. We evaluated the relationships between climate, environmental, and landscape attributes and the body size of different taxa of freshwater trout (Salmonidae) in the USA. Hierarchical spatial modeling across a gradient of habitats (5221 sites) illustrated the importance of watershed effects, which explained 17%–45% of the of the variation in body size across taxa. Stream size had a strong, positive relationship with body size, yet there was approximately tenfold difference in the strength of the relationship across taxa. Trout body size consistently declined with increasing density across taxa. Despite reliance on cold water, we found positive relationships between summer stream temperature and trout body size across most taxa. Our results highlight how providing trout access to larger, productive rivers for the expression of growth and life-history variation would promote body size diversity within and across populations.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2021-0343","usgsCitation":"Al-Chokhachy, R.K., Letcher, B., Muhlfeld, C.C., Dunham, J., Cline, T.J., Hitt, N.P., Roberts, J., and Schmetterling, D., 2022, Stream size, temperature, and density explain body sizes of freshwater salmonids across a range of climate conditions: Canadian Journal of Fisheries and Aquatic Sciences, v. 79, no. 10, p. 1729-1744, https://doi.org/10.1139/cjfas-2021-0343.","productDescription":"16 p.","startPage":"1729","endPage":"1744","ipdsId":"IP-131094","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":447627,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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jroberts@usgs.gov","orcid":"https://orcid.org/0000-0002-4193-610X","contributorId":5453,"corporation":false,"usgs":true,"family":"Roberts","given":"James","email":"jroberts@usgs.gov","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":859453,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Schmetterling, David","contributorId":196555,"corporation":false,"usgs":false,"family":"Schmetterling","given":"David","affiliations":[],"preferred":false,"id":859454,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70231929,"text":"70231929 - 2022 - Plant pathogens provide clues to the potential origin of bat white-nose syndrome Pseudogymnoascus destructans","interactions":[],"lastModifiedDate":"2022-06-16T15:31:33.624892","indexId":"70231929","displayToPublicDate":"2022-05-30T08:47:02","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3698,"text":"Virulence","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Plant pathogens provide clues to the potential origin of bat white-nose syndrome Pseudogymnoascus destructans","title":"Plant pathogens provide clues to the potential origin of bat white-nose syndrome Pseudogymnoascus destructans","docAbstract":"White-nose syndrome has killed millions of bats, yet both the origins and infection strategy of the causative fungus, Pseudogymnoascus destructans, remain elusive. We provide evidence for a novel hypothesis that P. destructans emerged from plant-associated fungi and retained invasion strategies affiliated with fungal pathogens of plants. We demonstrate that P. destructans invades bat skin in successive biotrophic and necrotrophic stages (hemibiotrophic infection), a mechanism previously only described in plant fungal pathogens. Further, the convergence of hyphae at hair follicles suggests nutrient tropism. Tropism, biotrophy, and necrotrophy are often associated with structures termed appressoria in plant fungal pathogens; the penetrating hyphae produced by P. destructans resemble appressoria. Finally, we conducted a phylogenomic analysis of a taxonomically diverse collection of fungi. Despite gaps in genetic sampling of prehistoric and contemporary fungal species, we estimate an 88% probability the ancestral state of the clade containing P. destructans was a plant-associated fungus.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/21505594.2022.2082139","usgsCitation":"Meteyer, C., Dutheil, J.Y., Keel, M.K., Boyles, J., and Stukenbrock, E.H., 2022, Plant pathogens provide clues to the potential origin of bat white-nose syndrome Pseudogymnoascus destructans: Virulence, v. 13, no. 1, p. 1020-1031, https://doi.org/10.1080/21505594.2022.2082139.","productDescription":"12 p.","startPage":"1020","endPage":"1031","ipdsId":"IP-124944","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":447631,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1080/21505594.2022.2082139","text":"Publisher Index Page"},{"id":401681,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"13","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-06-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Meteyer, Carol 0000-0002-4007-3410","orcid":"https://orcid.org/0000-0002-4007-3410","contributorId":207215,"corporation":false,"usgs":true,"family":"Meteyer","given":"Carol","affiliations":[{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":844133,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dutheil, Julien Yann","contributorId":292251,"corporation":false,"usgs":false,"family":"Dutheil","given":"Julien","email":"","middleInitial":"Yann","affiliations":[{"id":62846,"text":"Molecular Systems Evolution, Max Planck Institute for Evolutionary Biology, 24306 Plön, Germany.","active":true,"usgs":false}],"preferred":false,"id":844134,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Keel, M. Kevin","contributorId":127729,"corporation":false,"usgs":false,"family":"Keel","given":"M.","email":"","middleInitial":"Kevin","affiliations":[{"id":7127,"text":"2Southeastern Cooperative Wildlife Disease Study, College of Veterinary Medicine, University of Georgia, Athens, GA 30602, USA.","active":true,"usgs":false}],"preferred":false,"id":844135,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Boyles, Justin G.","contributorId":292252,"corporation":false,"usgs":false,"family":"Boyles","given":"Justin G.","affiliations":[{"id":62849,"text":"School of Biological Sciences, Southern Illinois University, Carbondale, Ill 62901","active":true,"usgs":false}],"preferred":false,"id":844136,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stukenbrock, Eva Holtgrewe","contributorId":292253,"corporation":false,"usgs":false,"family":"Stukenbrock","given":"Eva","email":"","middleInitial":"Holtgrewe","affiliations":[{"id":62850,"text":"5Max Planck Institute for Evolutionary Biology, 24306 Plön, Germany","active":true,"usgs":false}],"preferred":false,"id":844137,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70255807,"text":"70255807 - 2022 - Comparison of Digital Terrain Models from two photoclinometry methods","interactions":[],"lastModifiedDate":"2024-07-05T12:12:52.867955","indexId":"70255807","displayToPublicDate":"2022-05-30T07:04:59","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12997,"text":"International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of Digital Terrain Models from two photoclinometry methods","docAbstract":"We evaluate the horizontal resolution and vertical precision for digital topographic models (DTMs) of the Moon derived from image radiance information, a process known as photoclinometry (PC) or shape-from-shading (SfS). We use the implementations in two available planetary image processing software systems, single image PC in the U.S. Geological Survey Integrated Software for Imagers and Spectrometers (ISIS) system, and multi-image SfS in the Ames Stereo Pipeline (ASP), and test results obtained with and without use of a starting solution from stereo, with single and multiple images, and for varying illumination conditions. To obtain the higher quality reference DTMs against which the products can be evaluated, we derived DTMs by stereoanalysis of Lunar Reconnaissance Orbiter Narrow-Angle Camera (LROC NAC) images at their native pixel spacing of ∼0.5 m, then produced a 16-m/post stereo DTM from images downsampled to 4 m/pixel and refined it with images at 16 m/pixel. When used with a single image, both algorithms improved resolution (by a factor of 1.4 for PC and 2.4 for SfS compared to stereo). An albedo map produced in ISIS by ratioing the image to a simulation based on the stereo DTM was well correlated with one output by SfS. The albedo correction was crucial for PC with ∼60° incidence but not at ∼80°. DTMs produced by PC and SfS without a starting stereo DTM had larger errors but good detail, and could be useful for many applications. In SfS, it was necessary to increase smoothing to get a usable DTM when the weighting on an a priori DTM was reduced. Multi-image SfS including modeling of spatially varying albedo reduced vertical errors by factors of 1.5 or more compared to single-image SfS.
This paper documents a reversal in the groundwater salinity depth gradient in the North Coles Levee Oil Field in the San Joaquin Valley, California. Salinity, measured in mg/L, was mapped with water quality data from groundwater and oil and gas wells and salinity estimated from oil and gas well borehole geophysical logs using Archie's equation. The resulting three-dimensional salinity volume shows groundwater salinity increasing with depth through the Tulare and San Joaquin Formations to about 50,000 mg/L at 1100 m depth, then decreasing to 10,000–31,000 mg/L in the Etchegoin Formation at 1400 m depth. The high salinity zone occurs near the base of the San Joaquin Formation in sand lenses in shales that have been interpreted as representing a mudflat environment. The groundwater and produced water geochemistry show formation waters lie on the seawater dilution line, indicating the salinity structure is largely the result of dilution or evaporation of seawater and not due to water–rock interactions. Instead, changing depositional environments linked to decreasing sea level may be responsible for variably saline water at or near the time of deposition, leading to a salinity reversal preserved in connate waters. The steepness of the salinity reversal varies laterally, possibly due to post-depositional freshwater recharge allowed by thick sands, alternatively, by a change in connate water composition due to a lateral facies change present at the time of deposition. These results illustrate geologic and paleogeographic processes that drive the vertical salinity structure of groundwater in shallow alluvial basins.
Rare earth elements and yttrium (REYs) are critical elements and valuable commodities due to their limited availability and high demand in a wide range of applications and especially in high-technology products. The increased demand and geopolitical pressures motivate the search for alternative sources of REYs, and coal, coal waste, and coal ash are considered as new sources for these critical elements. This research evaluates the REY potential of coals from Indiana (USA). However, although coal data revealed REY potential, it suffered from sparse samples with complete REY measurements. Therefore, we explore the applicability of machine learning (ML) models and data augmentation techniques to demonstrate their applicability to evaluate REY potential in Indiana, and other areas in coal basins, using selected coal parameters (Al2O3, Fe2O3, C, Ash, S, P, Mo, Zn, and As contents) as covariates (indicators). Due to the relatively small sample size with complete REY data in the Indiana Coal Database, two data augmentation techniques (Random Over-Sampling Examples and Synthetic Minority Over-Sampling Technique) were used. Four machine learning algorithms (linear discriminate analysis, support vector machine, random forest, and artificial neural networks) were applied for modeling REY potential as a classification problem. The results show that application of Synthetic Minority Over-Sampling Technique prior to development of the support vector machine (SVM) models generated the best REY classification with an accuracy of 95%. The encouraging results based on Indiana coal data may suggest that a similar approach can be used for other coal basins for screening the locations with REY potential. Those locations then can be targeted for more detailed geochemical surveys to identify most promising areas and evaluate overall REY resources.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2022.104054","usgsCitation":"Chatterjee, S., Mastalerz, M., Drobniak, A., and Karacan, C.O., 2022, Machine learning and data augmentation approach for identification of rare earth element potential in Indiana Coals, USA: International Journal of Coal Geology, v. 259, 104054, 14 p., https://doi.org/10.1016/j.coal.2022.104054.","productDescription":"104054, 14 p.","ipdsId":"IP-138032","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":402804,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Indiana","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.56103515625,\n 40.49709237269567\n ],\n [\n -87.5390625,\n 39.35129035526705\n ],\n [\n -87.56103515625,\n 38.839707613545144\n ],\n [\n -87.86865234374999,\n 38.06539235133249\n ],\n [\n -88.11035156249999,\n 37.90953361677018\n ],\n [\n -88.154296875,\n 37.77071473849609\n ],\n [\n -87.451171875,\n 37.92686760148135\n ],\n [\n -87.099609375,\n 37.87485339352928\n ],\n [\n -86.81396484375,\n 38.048091067457236\n ],\n [\n -86.572265625,\n 37.89219554724437\n ],\n [\n -86.396484375,\n 38.11727165830543\n ],\n [\n -86.63818359375,\n 38.95940879245423\n ],\n [\n -86.8359375,\n 40.111688665595956\n ],\n [\n -87.03369140625,\n 40.463666324587685\n ],\n [\n -87.56103515625,\n 40.49709237269567\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"259","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Chatterjee, Snahamoy","contributorId":292652,"corporation":false,"usgs":false,"family":"Chatterjee","given":"Snahamoy","email":"","affiliations":[{"id":16203,"text":"Michigan Technological university","active":true,"usgs":false}],"preferred":false,"id":845399,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mastalerz, Maria","contributorId":292654,"corporation":false,"usgs":false,"family":"Mastalerz","given":"Maria","affiliations":[{"id":62959,"text":"IU and Indiana Geological Survey","active":true,"usgs":false}],"preferred":false,"id":845400,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Drobniak, Agnieszka","contributorId":292655,"corporation":false,"usgs":false,"family":"Drobniak","given":"Agnieszka","email":"","affiliations":[{"id":62959,"text":"IU and Indiana Geological Survey","active":true,"usgs":false}],"preferred":false,"id":845401,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Karacan, C. Ozgen 0000-0002-0947-8241","orcid":"https://orcid.org/0000-0002-0947-8241","contributorId":201991,"corporation":false,"usgs":true,"family":"Karacan","given":"C.","email":"","middleInitial":"Ozgen","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":845402,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70256676,"text":"70256676 - 2022 - Modeling spatiotemporal abundance and movement dynamics using an integrated spatial capture–recapture movement model","interactions":[],"lastModifiedDate":"2024-08-30T15:04:17.421329","indexId":"70256676","displayToPublicDate":"2022-05-28T09:39:55","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Modeling spatiotemporal abundance and movement dynamics using an integrated spatial capture–recapture movement model","docAbstract":"Animal movement is a fundamental ecological process affecting the survival and reproduction of individuals, the structure of populations, and the dynamics of communities. Methods to quantify animal movement and spatiotemporal abundances, however, are generally separate and therefore omit linkages between individual-level and population-level processes. We describe an integrated spatial capture–recapture (SCR) movement model to jointly estimate (1) the number and distribution of individuals in a defined spatial region and (2) movement of those individuals through time. We applied our model to a study of polar bears (Ursus maritimus) in a 28,125 km2 survey area of the eastern Chukchi Sea, USA in 2015 that incorporated capture–recapture and telemetry data. In simulation studies, the model provided unbiased estimates of movement, abundance, and detection parameters using a bivariate normal random walk and correlated random walk movement process. Our case study provided detailed evidence of directional movement persistence for both male and female bears, where individuals regularly traversed areas larger than the survey area during the 36-day study period. Scaling from individual- to population-level inferences, we found that densities varied from <0.75 bears/625 km2 grid cell/day in nearshore cells to 1.6–2.5 bears/grid cell/day for cells surrounded by sea ice. Daily abundance estimates ranged from 53 to 69 bears, with no trend across days. The cumulative number of unique bears that used the survey area increased through time due to movements into and out of the area, resulting in an estimated 171 individuals using the survey area during the study (95% credible interval 124–250). Abundance estimates were similar to a previous multiyear integrated population model using capture–recapture and telemetry data (2008–2016; Regehr et al., Scientific Reports 8:16780, 2018). Overall, the SCR–movement model successfully quantified both individual- and population-level space use, including the effects of landscape characteristics on movement, abundance, and detection, while linking the movement and abundance processes to directly estimate density within a prescribed spatial region and temporal period. Integrated SCR–movement models provide a generalizable approach to incorporate greater movement realism into population dynamics and link movement to emergent properties including spatiotemporal densities and abundances.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.3772","usgsCitation":"Hostetter, N.J., Regehr, E., Wilson, R., Royle, A., and Converse, S.J., 2022, Modeling spatiotemporal abundance and movement dynamics using an integrated spatial capture–recapture movement model: Ecology, v. 103, no. 10, e3772, 13 p., https://doi.org/10.1002/ecy.3772.","productDescription":"e3772, 13 p.","ipdsId":"IP-130471","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":447644,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecy.3772","text":"Publisher Index Page"},{"id":433369,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Russia, United States","otherGeospatial":"eastern Chukchi Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -176.16804284685193,\n 70.64271222820804\n ],\n [\n -176.4405798143377,\n 63.26571385602864\n ],\n [\n -160.5704824801017,\n 63.43804844317145\n ],\n [\n -160.5759226643521,\n 70.4273607365736\n ],\n [\n -176.16804284685193,\n 70.64271222820804\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"103","issue":"10","noUsgsAuthors":false,"publicationDate":"2022-07-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Hostetter, Nathan J. 0000-0001-6075-2157 nhostetter@usgs.gov","orcid":"https://orcid.org/0000-0001-6075-2157","contributorId":198843,"corporation":false,"usgs":true,"family":"Hostetter","given":"Nathan","email":"nhostetter@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":908609,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Regehr, E.V.","contributorId":341555,"corporation":false,"usgs":false,"family":"Regehr","given":"E.V.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":908610,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilson, R.R.","contributorId":341556,"corporation":false,"usgs":false,"family":"Wilson","given":"R.R.","affiliations":[{"id":40296,"text":"United States Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":908611,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":146229,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":908612,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":908613,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70231912,"text":"70231912 - 2022 - Toxicological responses to sublethal anticoagulant rodenticide exposure in free-flying hawks","interactions":[],"lastModifiedDate":"2022-10-17T15:31:20.593592","indexId":"70231912","displayToPublicDate":"2022-05-28T08:58:40","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1564,"text":"Environmental Science and Pollution Research","active":true,"publicationSubtype":{"id":10}},"title":"Toxicological responses to sublethal anticoagulant rodenticide exposure in free-flying hawks","docAbstract":"An important component of assessing the hazards of anticoagulant rodenticides to non-target wildlife is observations in exposed free-ranging individuals. The objective of this study was to determine whether environmentally realistic, sublethal first-generation anticoagulant rodenticide (FGAR) exposures via prey can result in direct or indirect adverse effects to free-flying raptors. We offered black-tailed prairie dogs (Cynomys ludovicianus) that had fed on Rozol® Prairie Dog Bait (Rozol, 0.005% active ingredient chlorophacinone, CPN) to six wild-caught red-tailed hawks (RTHA, Buteo jamaicensis), and also offered black-tailed prairie dogs that were not exposed to Rozol to another two wild-caught RTHAs for 7 days. On day 6, blood was collected to determine CPN’s effects on blood clotting time. On day 7, seven of the eight RTHAs were fitted with VHF radio telemetry transmitters and the RTHAs were released the following day and were monitored for 33 days. Prothrombin time (PT) and Russell’s viper venom time confirmed that the CPN-exposed RTHAs were exposed to and were adversely affected by CPN. Four of the six CPN-exposed RTHAs exhibited ptiloerection, an indication of thermoregulatory dysfunction due to CPN toxicity, but no signs of intoxication were observed in the reference hawk or the remaining two CPN-exposed RTHAs. Of note is that PT values were associated with ptiloerection duration and frequency; therefore, sublethal CPN exposure can directly or indirectly evoke adverse effects in wild birds. Although our sample sizes were small, this study is a first to relate coagulation times to adverse clinical signs in free-ranging birds.
","language":"English","publisher":"Springer","doi":"10.1007/s11356-022-20881-z","usgsCitation":"Vyas, N.B., Rattner, B.A., Lockhart, J.M., Hulse, C., Rice, C., Kuncir, F., and Kritz, K., 2022, Toxicological responses to sublethal anticoagulant rodenticide exposure in free-flying hawks: Environmental Science and Pollution Research, v. 29, p. 74024-74037, https://doi.org/10.1007/s11356-022-20881-z.","productDescription":"14 p.","startPage":"74024","endPage":"74037","ipdsId":"IP-129660","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":401682,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Rocky Mountain Arsenal National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -104.90089416503906,\n 39.80220607474971\n ],\n [\n -104.78897094726562,\n 39.80115102364283\n ],\n [\n -104.79034423828125,\n 39.86969567045658\n ],\n [\n -104.86518859863281,\n 39.86969567045658\n ],\n [\n -104.8974609375,\n 39.84281323262067\n ],\n [\n -104.90226745605469,\n 39.828577091142016\n ],\n [\n -104.90089416503906,\n 39.80220607474971\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"29","noUsgsAuthors":false,"publicationDate":"2022-05-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Vyas, Nimish B. 0000-0003-0191-1319 nvyas@usgs.gov","orcid":"https://orcid.org/0000-0003-0191-1319","contributorId":4494,"corporation":false,"usgs":true,"family":"Vyas","given":"Nimish","email":"nvyas@usgs.gov","middleInitial":"B.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":844099,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rattner, Barnett A. 0000-0003-3676-2843 brattner@usgs.gov","orcid":"https://orcid.org/0000-0003-3676-2843","contributorId":4142,"corporation":false,"usgs":true,"family":"Rattner","given":"Barnett","email":"brattner@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":844100,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lockhart, J. Michael","contributorId":179117,"corporation":false,"usgs":false,"family":"Lockhart","given":"J.","email":"","middleInitial":"Michael","affiliations":[],"preferred":false,"id":844101,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hulse, Craig S.","contributorId":292224,"corporation":false,"usgs":false,"family":"Hulse","given":"Craig S.","affiliations":[{"id":37374,"text":"Retired USGS","active":true,"usgs":false}],"preferred":false,"id":844102,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rice, Clifford P.","contributorId":270789,"corporation":false,"usgs":false,"family":"Rice","given":"Clifford P.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":844103,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kuncir, Frank","contributorId":139801,"corporation":false,"usgs":false,"family":"Kuncir","given":"Frank","email":"","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":844104,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kritz, Kevin","contributorId":292226,"corporation":false,"usgs":false,"family":"Kritz","given":"Kevin","email":"","affiliations":[{"id":7199,"text":"US FWS","active":true,"usgs":false}],"preferred":false,"id":844105,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70232428,"text":"70232428 - 2022 - Watching the Cryosphere thaw: Seismic monitoring of permafrost degradation using distributed acoustic sensing during a controlled heating experiment","interactions":[],"lastModifiedDate":"2022-07-01T12:29:14.282307","indexId":"70232428","displayToPublicDate":"2022-05-28T07:27:47","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Watching the Cryosphere thaw: Seismic monitoring of permafrost degradation using distributed acoustic sensing during a controlled heating experiment","docAbstract":"Permafrost degradation is rapidly increasing in response to a warming Arctic climate, altering landscapes and damaging critical infrastructure. Solutions for monitoring permafrost thaw dynamics are essential to understand biogeochemical feedbacks as well as to issue warnings for hazardous geotechnical conditions. We investigate the feasibility of permafrost monitoring using permanently installed fiber-optic seismic networks. We conducted a 2-month seismic monitoring campaign during a controlled thaw experiment using a permanent surface orbital vibrator (SOV) and a 2D-array of distributed acoustic sensing (DAS) cables, and observed significant (15%) shear-wave velocity (Vs) reductions and approximately 2 m depression of the permafrost table beneath the heating zone. These observations were validated by time-lapse horizontal-to-vertical spectral ratio (HVSR) analysis from three co-located broadband seismometers. The combination of SOV and DAS provided unique seismic observations for permafrost monitoring at the field scale, as well as a basis for design and development of early warning systems for permafrost thaw.
We use the Multiple Element Limitation (MEL) model to examine responses of twelve ecosystems to elevated carbon dioxide (CO2), warming, and 20% decreases or increases in precipitation. Ecosystems respond synergistically to elevated CO2, warming, and decreased precipitation combined because higher water-use efficiency with elevated CO2 and higher fertility with warming compensate for responses to drought. Response to elevated CO2, warming, and increased precipitation combined is additive. We analyze changes in ecosystem carbon (C) based on four nitrogen (N) and four phosphorus (P) attribution factors: (1) changes in total ecosystem N and P, (2) changes in N and P distribution between vegetation and soil, (3) changes in vegetation C:N and C:P ratios, and (4) changes in soil C:N and C:P ratios. In the combined CO2 and climate change simulations, all ecosystems gain C. The contributions of these four attribution factors to changes in ecosystem C storage varies among ecosystems because of differences in the initial distributions of N and P between vegetation and soil and the openness of the ecosystem N and P cycles. The net transfer of N and P from soil to vegetation dominates the C response of forests. For tundra and grasslands, the C gain is also associated with increased soil C:N and C:P. In ecosystems with symbiotic N fixation, C gains resulted from N accumulation. Because of differences in N versus P cycle openness and the distribution of organic matter between vegetation and soil, changes in the N and P attribution factors do not always parallel one another. Differences among ecosystems in C-nutrient interactions and the amount of woody biomass interact to shape ecosystem C sequestration under simulated global change. We suggest that future studies quantify the openness of the N and P cycles and changes in the distribution of C, N, and P among ecosystem components, which currently limit understanding of nutrient effects on C sequestration and responses to elevated CO2 and climate change.
This report describes the findings of a U.S. Geological Survey study, completed in cooperation with the Bureau of Land Management, focused on better understanding the present-day (1975–2019) hydrogeology and groundwater quality of the San Agustin Basin in west-central New Mexico to support sustainable groundwater resource management. The basin hosts a relatively undeveloped basin-fill and alluvium aquifer system and is topographically divided into east and west subbasins by the McClure Hills. Groundwater chemistry and groundwater elevation data were compiled, collected, and interpreted in the context of groundwater flow and quality. The analyses presented in this report consider groundwater chemistry data collected within the last decade (2010–19) and groundwater elevation data collected from 1975 through 2019 to provide insight into present-day conditions. Groundwater elevations show that groundwater typically moves from the highlands to the lowlands, with a prominent east to west regional trend. Groundwater elevations were lowest in the southwestern portion of the west subbasin, where estimated flow directions suggest underflow through the local highlands into the northern East Fork Gila River watershed, which is further supported by historical groundwater elevation data from the northern East Fork Gila River watershed. Gradual groundwater elevation gradients (about 2 feet per mile) near the east and west subbasin divide suggest that groundwater slowly flows from the east subbasin to the west subbasin.
Quantitative analyses of groundwater chemistry data show that groundwater in both subbasins has similar chemical characteristics. A systematic east to west groundwater evolution in water chemistry was not observed despite evidenced subbasin connectivity. The absence of this pattern suggests that groundwater mixing is regionally prevalent, sediment reactivity is low and variable, and (or) recharge conditions are comparable in both subbasins. Groundwater chemistry was generally independent of aquifer type, suggesting that the aquifers are hydrologically well connected. Corrected carbon-14 groundwater age estimates in the basin ranged from 232 to 13,916 years before present with a median of 5,409 years. A wide range of groundwater ages is therefore present in the basin, with waters commonly being thousands of years old, thereby supporting generally slow regional groundwater movement. A component of relatively young groundwater, for which estimated ages could not be accurately computed, is also present in the basin, and it may commonly mix with older waters. The spatial distribution of categorical and quantitative groundwater ages indicates that most recharge likely occurs in the highlands through mountain-block recharge and as focused recharge within arroyos, although evidence of modern (1953 and after) groundwater was minimal at sampled sites.
Median annual gradients (groundwater elevation change over time) indicate that most groundwater elevations in the lowlands changed little (−0.2 to 0.2 foot per year) from 1975 through 2019. Groundwater elevations in the highlands varied more annually, which is likely due to recharge from precipitation events. These more variable groundwater elevations in the highlands compared with the lowlands, along with groundwater ages, provide further evidence that most groundwater recharge takes place in the highlands, with minimal recharge in the lowlands. Median groundwater elevation change for all sites was −0.05 foot per year. Temporal consistency of lowland groundwater elevations suggests that regional groundwater dynamics have been more or less stable through time under current climate and development conditions, although median annual gradients indicate that groundwater elevations may have slightly declined on average between 1975 and 2019.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225029","collaboration":"Prepared in cooperation with Bureau of Land Management and in collaboration with New Mexico Bureau of Geology and Mineral Resources","usgsCitation":"Pepin, J.D., Travis, R.E., Blake, J.M., Rinehart, A., and Koning, D., 2022, Hydrogeology and groundwater quality in the San Agustin Basin, New Mexico, 1975–2019: U.S. Geological Survey Scientific Investigations Report 2022–5029, 61 p., 4 app., https://doi.org/10.3133/sir20225029.","productDescription":"Report: x, 61 p.; 6 Tables; Dataset","numberOfPages":"76","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-120066","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":502386,"rank":12,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113080.htm","linkFileType":{"id":5,"text":"html"}},{"id":401145,"rank":11,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":401143,"rank":10,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029_table3.1.csv","text":"Table 3.1","size":"29.5 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2022-5029 Table 3.1"},{"id":401142,"rank":9,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029_table3.1.xlsx","text":"Table 3.1","size":"55.2 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2022-5029 Table 3.1"},{"id":401141,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029_table2.1.csv","text":"Table 2.1","size":"14.3 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2022-5029 Table 2.1"},{"id":401140,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029_table2.1.xlsx","text":"Table 2.1","size":"27.6 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2022-5029 Table 2.1"},{"id":401138,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029_table1.1.xlsx","text":"Table 1.1","size":"116 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2022-5029 Table 1.1"},{"id":401137,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5029/images"},{"id":401134,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5029/coverthb.jpg"},{"id":401135,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029.pdf","text":"Report","size":"8.37 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5029"},{"id":401139,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029_table1.1.csv","text":"Table 1.1","size":"146 kB","linkFileType":{"id":7,"text":"csv"},"description":"SIR 2022-5029 Table 1.1"},{"id":401136,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5029/sir20225029.XML"}],"country":"United States","state":"New Mexico","otherGeospatial":"San Agustin Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.666,\n 34.5\n ],\n [\n -107.333,\n 34.5\n ],\n [\n -107.333,\n 33.333\n ],\n [\n -108.666,\n 33.333\n ],\n [\n -108.666,\n 34.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113