Long-term (>20 y) suspended sediment (SS) and particulate organic carbon (POC) records are relatively rare and yet are necessary for understanding linkages between climate, erosion and carbon export. We estimated long-term (>23 y) SS and POC yields from four nested catchments that ranged from <1 to 54 km2 in area across the Reynolds Creek Experimental Watershed and Critical Zone Observatory (RCEW-CZO) in southwestern Idaho, USA. We found strong relationships between log10SS and log10POC (R2 = 0.38–0.86) that varied across catchments but remained robust across years, one dry and one of the wettest water years on record. Mean annual SS yields varied from 18 to 89 g SS m−2 y−1 and POC from 0.6 to 11.0 g C m−2 y−1 across the four catchments. Water yield explained much of the temporal variation (72%–85%) in SS and POC yields except in a small, snow-dominated headwater catchment where it explained 15%–51%. The largest five water years accounted for 69%–84% of the total SS and POC yields in catchments with 24 y records. All catchments had positive slopes (>0) for SS and POC concentration-discharge (C-Q) relationships, with large catchments exhibiting greater slopes (0.66–0.97) than smaller ones (0.14–0.16). In addition, most catchments were dominated (80%) by clockwise hysteretic curves. Lack of seasonal exhaustion in the SS-POC relationships, positive C-Q and clockwise relations indicated that these systems were transport-rather than supply limited, and that sediment and POC appeared to be sourced from channel/bank erosion and remobilization. POC yields represent 1%–10% of mean water year net ecosystem exchange depending on elevation; lower elevation catchments may shift from being carbon sinks to sources after accounting for fluvial POC export associated with changes in climate.
Predator–prey interactions shape ecosystems and can help maintain biodiversity. However, for many of the earth's most biodiverse and abundant organisms, including terrestrial arthropods, these interactions are difficult or impossible to observe directly with traditional approaches. Based on previous theory, it is likely that predator–prey interactions for these organisms are shaped by a combination of predator traits, including body size and species-specific hunting strategies. In this study, we combined diet DNA metabarcoding data of 173 individual invertebrate predators from nine species (a total of 305 individual predator–prey interactions) with an extensive community body size data set of a well-described invertebrate community to explore how predator traits and identity shape interactions. We found that (1) mean size of prey families in the field usually scaled with predator size, with species-specific variation to a general size-scaling relationship (exceptions likely indicating scavenging or feeding on smaller life stages). We also found that (2) although predator hunting traits, including web and venom use, are thought to shape predator–prey interaction outcomes, predator identity more strongly influenced our indirect measure of the relative size of predators and prey (predator:prey size ratios) than either of these hunting traits. Our findings indicate that predator body size and species identity are important in shaping trophic interactions in invertebrate food webs and could help predict how anthropogenic biodiversity change will influence terrestrial invertebrates, the earth's most diverse animal taxonomic group.
The SuperCam instrument on the Perseverance Mars 2020 rover uses a pulsed 1064 nm laser to ablate targets at a distance and conduct laser induced breakdown spectroscopy (LIBS) by analyzing the light from the resulting plasma. SuperCam LIBS spectra are preprocessed to remove ambient light, noise, and the continuum signal present in LIBS observations. Prior to quantification, spectra are masked to remove noisier spectrometer regions and spectra are normalized to minimize signal fluctuations and effects of target distance. In some cases, the spectra are also standardized or binned prior to quantification. To determine quantitative elemental compositions of diverse geologic materials at Jezero crater, Mars, we use a suite of 1198 laboratory spectra of 334 well-characterized reference samples. The samples were selected to span a wide range of compositions and include typical silicate rocks, pure minerals (e.g., silicates, sulfates, carbonates, oxides), more unusual compositions (e.g., Mn ore and sodalite), and replicates of the sintered SuperCam calibration targets (SCCTs) onboard the rover. For each major element (SiO2, TiO2, Al2O3, FeOT, MgO, CaO, Na2O, K2O), the database was subdivided into five “folds” with similar distributions of the element of interest. One fold was held out as an independent test set, and the remaining four folds were used to optimize multivariate regression models relating the spectrum to the composition. We considered a variety of models, and selected several for further investigation for each element, based primarily on the root mean squared error of prediction (RMSEP) on the test set, when analyzed at 3 m. In cases with several models of comparable performance at 3 m, we incorporated the SCCT performance at different distances to choose the preferred model. Shortly after landing on Mars and collecting initial spectra of geologic targets, we selected one model per element. Subsequently, with additional data from geologic targets, some models were revised to ensure results that are more consistent with geochemical constraints. The calibration discussed here is a snapshot of an ongoing effort to deliver the most accurate chemical compositions with SuperCam LIBS.
No abstract available.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.2c00052","usgsCitation":"Manceau, A., Brossier, R., Janssen, S., and Poulin, B., 2022, Response to comment on “Mercury isotope fractionation by internal demethylation and biomineralization reactions in seabirds: Implications for environmental mercury science”: Principles and limitations of source tracing and process tracing with stable isotope signatures: Environmental Science and Technology, v. 56, no. 3, p. 2065-2068, https://doi.org/10.1021/acs.est.2c00052.","productDescription":"4 p.","startPage":"2065","endPage":"2068","ipdsId":"IP-136180","costCenters":[{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":449086,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hal.science/hal-03688547","text":"External Repository"},{"id":394752,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"56","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-01-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Manceau, Alain 0000-0003-0845-611X","orcid":"https://orcid.org/0000-0003-0845-611X","contributorId":194255,"corporation":false,"usgs":false,"family":"Manceau","given":"Alain","email":"","affiliations":[],"preferred":false,"id":831520,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brossier, Romain 0000-0002-7195-8123","orcid":"https://orcid.org/0000-0002-7195-8123","contributorId":267387,"corporation":false,"usgs":false,"family":"Brossier","given":"Romain","email":"","affiliations":[{"id":55486,"text":"University of Grenoble, France","active":true,"usgs":false}],"preferred":false,"id":831521,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Janssen, Sarah E. 0000-0003-4432-3154","orcid":"https://orcid.org/0000-0003-4432-3154","contributorId":210991,"corporation":false,"usgs":true,"family":"Janssen","given":"Sarah E.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":831522,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Poulin, Brett 0000-0002-5555-7733","orcid":"https://orcid.org/0000-0002-5555-7733","contributorId":260893,"corporation":false,"usgs":false,"family":"Poulin","given":"Brett","affiliations":[{"id":52706,"text":"Department of Environmental Toxicology, University of California Davis, Davis, CA 95616, USA","active":true,"usgs":false}],"preferred":false,"id":831523,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70228747,"text":"70228747 - 2022 - Factors affecting spatiotemporal variation in survival of endangered winter-run Chinook Salmon outmigrating from the Sacramento River","interactions":[],"lastModifiedDate":"2022-04-12T13:30:37.906018","indexId":"70228747","displayToPublicDate":"2022-01-21T06:39:28","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Factors affecting spatiotemporal variation in survival of endangered winter-run Chinook Salmon outmigrating from the Sacramento River","docAbstract":"Among four extant and declining Chinook salmon (Oncorhynchus tshawytscha) runs in California’s Central Valley, none have declined as precipitously as Sacramento River winter-run Chinook Salmon. In addition to habitat loss, migratory winter-run employ a life history strategy to reside and feed in stopover habitats on their way from freshwaters to the ocean. This life history strategy is widely considered to be a key factor in the continued decline of winter-run. Using acoustic telemetry, we examined conditions that influenced reach-specific movement and survival of outmigrating juveniles during a prolonged, multi-year drought from 2013-2016, followed by one of the wettest years on record in 2017. We modeled how time-varying individual riverine covariates and reach-specific habitat features influenced smolt survival. Model selection favored a model with mean annual flow, intra-annual deviations from the mean flow at the reach scale, reach-specific channel characteristics, and travel time. Mean annual flow had the strongest positive effect on survival. A negative interaction between mean annual flow and intra-annual reach flow indicated that within-year deviations at the reach scale from annual mean flow had larger effects on survival in low flow years. These factors resulted in higher survival in years with pulse flows or high flows. Changes in movement behavior in response to small scale changes in velocity were negatively associated with survival. Covariates of revetment and wooded bank habitat were positively associated with survival but the effect of these fixed habitat features changed depending on whether they were situated in the upper or lower part of the river. Fish exhibited density dependent stopover behavior, with slowed downstream migration in the upper river in the wet years and extending to the lower river in the most critically dry year. This paper contributes two key findings for natural resource managers interested in flow management and targeted habitat restoration. The first is new insight to how the magnitude of pulse flows in dry and wet years affect survival of winter-run. The second is that density dependence influences where stopover habitat is used. Despite this, we identified an area of the river where fish consistently exhibited stopover behavior in all years.
The details and mechanisms for Neogene river reorganization in the U.S. Pacific Northwest and northern Rocky Mountains have been debated for over a century with key implications for how tectonic and volcanic systems modulate topographic development. To evaluate paleo-drainage networks, we produced an expansive data set and provenance analysis of detrital zircon U-Pb ages from Miocene to Pleistocene fluvial strata along proposed proto-Snake and Columbia River pathways. Statistical comparisons of Miocene-Pliocene detrital zircon spectra do not support previously hypothesized drainage routes of the Snake River. We use detrital zircon unmixing models to test prior Snake River routes against a newly hypothesized route, in which the Snake River circumnavigated the northern Rocky Mountains and entered the Columbia Basin from the northeast prior to incision of Hells Canyon. Our proposed ancestral Snake River route best matches detrital zircon age spectra throughout the region. Furthermore, this northerly Snake River route satisfies and provides context for shifts in the sedimentology and fish faunal assemblages of the western Snake River Plain and Columbia Basin through Miocene−Pliocene time. We posit that eastward migration of the Yellowstone Hotspot and its effect on thermally induced buoyancy and topographic uplift, coupled with volcanic densification of the eastern Snake River Plain lithosphere, are the primary mechanisms for drainage reorganization and that the eastern and western Snake River Plain were isolated from one another until the early Pliocene. Following this basin integration, the substantial increase in drainage area to the western Snake River Plain likely overtopped a bedrock threshold that previously contained Lake Idaho, which led to incision of Hells Canyon and establishment of the modern Snake and Columbia River drainage network.
Historically, Cisco Coregonus artedi and deepwater ciscoes Coregonus spp. were the most abundant and ecologically important fish species in the Laurentian Great Lakes, but anthropogenic influences caused nearly all populations to collapse by the 1970s. Fishery managers have begun exploring the feasibility of restoring populations throughout the basin, but questions regarding hatchery propagation and stocking remain. We used historical and contemporary stock-recruit parameters previously estimated for Ciscoes in Wisconsin waters of Lake Superior, with estimates of age-1 Cisco rearing habitat (broadly defined as total ha ≤ 80 m depth) and natural mortality, to estimate how many fry (5.5 months post-hatch), fall fingerling (7.5 months post-hatch), and age-1 (at least 12 months post-hatch) hatchery-reared Ciscoes are needed for stocking in the Great Lakes to mimic recruitment rates in Lake Superior, a lake that has undergone some recovery. Estimated stocking densities suggested that basin-wide stocking would require at least 0.641-billion fry, 0.469-billion fall fingerlings, or 0.343-billion age-1 fish for a simultaneous restoration effort targeting historically important Cisco spawning and rearing areas in Lakes Huron, Michigan, Erie, Ontario, and Saint Clair. Numbers required for basin-wide stocking were considerably greater than current or planned coregonine production capacity, thus simultaneous stocking in the Great Lakes is likely not feasible. Provided current habitat conditions do not preclude Cisco restoration, managers could maximize the effectiveness of available production capacity by concentrating stocking efforts in historically important spawning and rearing areas, similar to the current stocking effort in Saginaw Bay, Lake Huron. Other historically important Cisco spawning and rearing areas within each lake (listed in no particular order) include: (1) Thunder Bay in Lake Huron, (2) Green Bay in Lake Michigan, (3) the islands near Sandusky, Ohio, in western Lake Erie, and (4) the area near Hamilton, Ontario, and Bay of Quinte in Lake Ontario. Our study focused entirely on Ciscoes but may provide a framework for describing future stocking needs for deepwater ciscoes.
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0000-0002-0331-9397","orcid":"https://orcid.org/0000-0002-0331-9397","contributorId":271207,"corporation":false,"usgs":false,"family":"Rook","given":"Benjamin","email":"","middleInitial":"J.","affiliations":[{"id":54519,"text":"U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":831190,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hansen, Michael J. 0000-0001-8522-3876 michaelhansen@usgs.gov","orcid":"https://orcid.org/0000-0001-8522-3876","contributorId":5006,"corporation":false,"usgs":true,"family":"Hansen","given":"Michael","email":"michaelhansen@usgs.gov","middleInitial":"J.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":831191,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bronte, Charles R.","contributorId":190727,"corporation":false,"usgs":false,"family":"Bronte","given":"Charles","email":"","middleInitial":"R.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":831192,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70230142,"text":"70230142 - 2022 - Transforming Palmyra Atoll to native-tree dominance will increase net carbon storage and reduce dissolved organic carbon reef runoff","interactions":[],"lastModifiedDate":"2022-03-30T12:26:43.197877","indexId":"70230142","displayToPublicDate":"2022-01-20T07:24:58","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Transforming Palmyra Atoll to native-tree dominance will increase net carbon storage and reduce dissolved organic carbon reef runoff","docAbstract":"Native forests on tropical islands have been displaced by non-native species, leading to calls for their transformation. Simultaneously, there is increasing recognition that tropical forests can help sequester carbon that would otherwise enter the atmosphere. However, it is unclear if native forests sequester more or less carbon than human-altered landscapes. At Palmyra Atoll, efforts are underway to transform the rainforest composition from coconut palm (Cocos nucifera) dominated to native mixed-species. To better understand how this landscape-level change will alter the atoll’s carbon dynamics, we used field sampling, remote sensing, and parameter estimates from the literature to model the total carbon accumulation potential of Palmyra’s forest before and after transformation. The model predicted that replacing the C. nucifera plantation with native species would reduce aboveground biomass from 692.6 to 433.3 Mg C. However, expansion of the native Pisonia grandis and Heliotropium foertherianum forest community projected an increase in soil carbon to at least 13,590.8 Mg C, thereby increasing the atoll’s overall terrestrial carbon storage potential by 11.6%. Nearshore sites adjacent to C. nucifera canopy had a higher dissolved organic carbon (DOC) concentration (110.0 μMC) than sites adjacent to native forest (81.5 μMC), suggesting that, in conjunction with an increase in terrestrial carbon storage, replacing C. nucifera with native forest will reduce the DOC exported from the forest into in nearshore marine habitats. Lower DOC levels have potential benefits for corals and coral dependent communities. For tropical islands like Palmyra, reverting from C. nucifera dominance to native tree dominance could buffer projected climate change impacts by increasing carbon storage and reducing coral disease.
Shark dermal scale (denticle) accumulation in the fossil record can provide information about the abundance and composition of past shark communities. Denticles are shed continuously, such that a single shark leaves a scattered composite of many isolated denticles in sediments. However, the rate of denticle shedding as well as how these rates vary among shark species with different life modes and their consistency over time are unknown, limiting the interpretation of denticle assemblages. To better understand the process of denticle shedding and calibrate the relationship between absolute shark abundance in the environment and denticle deposition in sediments, we captured denticles shed by 2 shark species in a large aquarium over 9 mo. We then simulated how these aquarium-derived shedding rates shape the relationship between shark abundance and denticle accumulation. Bonnethead sharks Sphyrna tiburo, a more active, benthopelagic species with small, thin denticles, shed 3.6 times faster on average than zebra sharks Stegostoma fasciatum, a more sedentary, demersal species with large, robust denticles. This pattern persisted when shedding rates were corrected by estimated denticle quantities, shark space use, and methodological factors (2.2- to 3.8-fold difference). Over the study, bonnethead shark shedding rates declined while zebra shark shedding rates increased slightly. Finally, denticle assemblage composition corresponded with the relative abundance of denticles on the body of each species, consistent with natural shedding rather than selective loss. Overall, we show that shark taxa contribute unevenly to the denticle record, indicating that shedding rate measurements can help inform and constrain ecological interpretations of denticle assemblages.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/meps13936","usgsCitation":"Dillon, E.M., Bagla, A., Plioplys, K.D., McCauley, D., Lafferty, K.D., and O’Dea, A., 2022, Dermal denticle shedding rates vary between two captive shark species: Marine Ecology Progress Series, v. 682, p. 153-167, https://doi.org/10.3354/meps13936.","productDescription":"15 p.","startPage":"153","endPage":"167","ipdsId":"IP-129960","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":413531,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"682","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dillon, Erin M.","contributorId":221878,"corporation":false,"usgs":false,"family":"Dillon","given":"Erin","email":"","middleInitial":"M.","affiliations":[{"id":34029,"text":"U.C. Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":865277,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bagla, Anshika","contributorId":302734,"corporation":false,"usgs":false,"family":"Bagla","given":"Anshika","email":"","affiliations":[{"id":37180,"text":"UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":865278,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Plioplys, Kiera D.","contributorId":302735,"corporation":false,"usgs":false,"family":"Plioplys","given":"Kiera","email":"","middleInitial":"D.","affiliations":[{"id":37180,"text":"UC Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":865279,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCauley, Douglas J.","contributorId":287056,"corporation":false,"usgs":false,"family":"McCauley","given":"Douglas J.","affiliations":[{"id":16936,"text":"University of California Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":865280,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lafferty, Kevin D. 0000-0001-7583-4593 klafferty@usgs.gov","orcid":"https://orcid.org/0000-0001-7583-4593","contributorId":1415,"corporation":false,"usgs":true,"family":"Lafferty","given":"Kevin","email":"klafferty@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":865281,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"O’Dea, Aaron","contributorId":302736,"corporation":false,"usgs":false,"family":"O’Dea","given":"Aaron","affiliations":[{"id":65541,"text":"Smithsonian Tropical Research Institute, University of Bologna","active":true,"usgs":false}],"preferred":false,"id":865282,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70227645,"text":"70227645 - 2022 - A biological condition gradient for Caribbean coral reefs: Part II. Numeric rules using sessile benthic organisms","interactions":[],"lastModifiedDate":"2022-01-24T13:07:15.729427","indexId":"70227645","displayToPublicDate":"2022-01-20T07:02:25","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"A biological condition gradient for Caribbean coral reefs: Part II. Numeric rules using sessile benthic organisms","docAbstract":"The Biological Condition Gradient (BCG) is a conceptual model used to describe incremental changes in biological condition along a gradient of increasing anthropogenic stress. As coral reefs collapse globally, scientists and managers are focused on how to sustain the crucial structure and functions, and the benefits that healthy coral reef ecosystems provide for many economies and societies. We developed a numeric (quantitative) BGC model for the coral reefs of Puerto Rico and the US Virgin Islands to transparently facilitate ecologically meaningful management decisions regarding these fragile resources. Here, reef conditions range from natural, undisturbed conditions to severely altered or degraded conditions. Numeric decision rules were developed by an expert panel for scleractinian corals and other benthic assemblages using multiple attributes to apply in shallow-water tropical fore reefs with depths <30 m. The numeric model employed decision rules based on metrics (e.g., % live coral cover, coral species richness, pollution-sensitive coral species, unproductive and sediment substrates, % cover by Orbicella spp.) used to assess coral reef condition. Model confirmation showed the numeric BCG model predicted the panel’s median site ratings for 84% of the sites used to calibrate the model and 89% of independent validation sites. The numeric BCG model is suitable for adaptive management applications and supports bioassessment and criteria development. It is a robust assessment tool that could be used to establish ecosystem condition that would aid resource managers in evaluating and communicating current or changing conditions, protect water and habitat quality in areas of high biological integrity, or develop restoration goals with stakeholders and other public beneficiaries.
This report summarizes the software and algorithms that are used to calibrate images returned by the High Resolution Imaging Science Experiment (HiRISE) camera onboard the Mars Reconnaissance Orbiter (MRO) spacecraft. The instrument design and data processing methods are summarized below, followed by a description of relevant calibration data and details of the calibration procedure. In this document, we describe the software that uses those coefficients and matrices to radiometrically calibrate HiRISE data. This software is included in version 3 of the Integrated Software for Imagers and Spectrometers (ISIS3), which is developed and maintained by the U.S. Geological Survey Astrogeology Science Center in Flagstaff, Ariz., for the international planetary science community via funding from the National Aeronautics and Space Administration. ISIS3 is freely available to the scientific community and can be obtained at http://isis.astrogeology.usgs.gov/index.html. Support for ISIS3 is provided at https://github.com/USGS-Astrogeology/ISIS3.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm7C27","usgsCitation":"Becker, K.J., Milazzo, M.P., Delamere, W.A., Herkenhoff, K.E., Eliason, E.M., Russell, P.S., Keszthelyi, L.P., and McEwen, A.S., 2021, hical—The HiRISE radiometric calibration software developed within the ISIS3 planetary image processing suite: U.S. Geological Survey Techniques and Methods, book 7, chap. C27, 23 p., https://doi.org/10.3133/tm7C27.","productDescription":"v, 23 p.","numberOfPages":"23","onlineOnly":"Y","ipdsId":"IP-069330","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":394544,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/07/c27/covrthb.jpg"},{"id":394545,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/07/c27/tm7c27.pdf","text":"Report","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"}}],"contact":"Contact Astrogeology Research Program staff
Astrogeology Science Center
U.S. Geological Survey
2255 N. Gemini Dr.
Flagstaff, AZ 86001
The Yucaipa groundwater subbasin (referred to in this report as the Yucaipa subbasin) is located about 75 miles (mi) east of of Los Angeles and about 12 mi southeast of the City of San Bernardino. In the Yucaipa subbasin, as in much of southern California, limited annual rainfall and large water demands can strain existing water supplies; therefore, understanding local surface water and groundwater conditions is essential for managing these resources. To better understand the hydrogeology and water resources in the Yucaipa subbasin, especially groundwater, the San Bernardino Valley Municipal Water District and the U.S. Geological Survey initiated a cooperative study to evaluate the hydrogeologic system of the Yucaipa subbasin and the encompassing Yucaipa Valley watershed. Previous studies of the area provided information on general geologic and hydrologic conditions, but this study provides the first comprehensive definition of the hydrogeology of the subsurface throughout the entire subbasin.
The Yucaipa subbasin is located between the northwest trending San Andreas fault zone and San Jacinto fault. Several northeast-trending dip-slip faults dissect the Yucaipa subbasin, providing the mechanism for structural relief within the sediment-filled subbasin and between the subbasin and surrounding mountains and highlands. Several of these dip-slip faults have been previously identified as potential barriers to groundwater flow. This report provides a synthesis of previous studies and a discussion of the geologic interpretations that were used as the foundation for hydrogeologic classification of the Yucaipa subbasin. Notably, this report (1) adopts the recently named and classified sedimentary deposits of Live Oak Canyon geologic formation and extends the mapped distribution of the formation into the Yucaipa subbasin, and (2) adopts the interpretation that activity along the Banning fault predates the deposition of most basin-fill sedimentary materials in the Yucaipa subbasin.
Four hydrogeologic units were classified in the Yucaipa subbasin: (1) crystalline basement, (2) consolidated sedimentary materials, (3) unconsolidated sediment, and (4) surficial materials. The crystalline basement unit forms the bottom boundary of the aquifer system, and the three other units comprise the basin-fill aquifer system. The four hydrogeologic units vary in extent, thickness, and structural relief across the subbasin, with the unconsolidated sediment unit serving as the primary aquifer unit. A three-dimensional hydrogeologic framework model was developed for the Yucaipa subbasin and surrounding area to characterize the thickness, extent, and hydrogeologic variability of the aquifer system. Geologic maps, borehole geophysical logs, drillers’ lithology logs, and depth-to-basement gravity data were used to map and interpolate the subsurface extent and structure of the hydrogeologic units within the subbasin. Faults and structures of geologic and (or) hydrogeologic importance were included in the model for future evaluation of their potential effects on groundwater flow. The resulting hydrogeologic framework is consistent with existing geologic concepts and the tectonic and structural history of the Yucaipa subbasin and surrounding area. The framework is also suitable for use in basin-scale hydrogeologic investigations.
Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Climate change is rapidly driving global biodiversity declines. How wetland macroinvertebrate assemblages are responding is unclear, a concern given their vital function in these ecosystems. Using a data set from 769 minimally impacted depressional wetlands across the globe (467 temporary and 302 permanent), we evaluated how temperature and precipitation (average, range, variability) affects the richness and beta diversity of 144 macroinvertebrate families. To test the effects of climatic predictors on macroinvertebrate diversity, we fitted generalized additive mixed-effects models (GAMM) for family richness and generalized dissimilarity models (GDMs) for total beta diversity. We found non-linear relationships between family richness, beta diversity, and climate. Maximum temperature was the main climatic driver of wetland macroinvertebrate richness and beta diversity, but precipitation seasonality was also important. Assemblage responses to climatic variables also depended on wetland water permanency. Permanent wetlands from warmer regions had higher family richness than temporary wetlands. Interestingly, wetlands in cooler and dry-warm regions had the lowest taxonomic richness, but both kinds of wetlands supported unique assemblages. Our study suggests that climate change will have multiple effects on wetlands and their macroinvertebrate diversity, mostly via increases in maximum temperature, but also through changes in patterns of precipitation. The most vulnerable wetlands to climate change are likely those located in warm-dry regions, where entire macroinvertebrate assemblages would be extirpated. Montane and high-latitude wetlands (i.e., cooler regions) are also vulnerable to climate change, but we do not expect entire extirpations at the family level.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.scitotenv.2022.153052","usgsCitation":"Epele, L., Grech, M.G., Williams-Subiza, E.A., Stenert, C., McLean, K., Greig, H., Maltchik, L., Pires, M.M., Bird, M.S., Boissezon, A., Boix, D., Demierre, E., García, P., Gascón, S., Jeffries, M., Kneitel, J.M., Loskutov, O., Manzo, L.M., Mataloni, G., Mlambo, M.C., Oertli, B., Sala, J., Scheibler, E.E., Wu, H., Wissinger, S., and Batzer, D., 2022, Perils of life on the edge: Climatic threats to global diversity patterns of wetland macroinvertebrates: Science of the Total Environment, v. 820, 153052, 10 p., https://doi.org/10.1016/j.scitotenv.2022.153052.","productDescription":"153052, 10 p.","ipdsId":"IP-127993","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":449106,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://nrl.northumbria.ac.uk/id/eprint/48317/1/STOTEN_Epele_Manuscript.pdf","text":"External Repository"},{"id":396114,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"820","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Epele, Luis B.","contributorId":279551,"corporation":false,"usgs":false,"family":"Epele","given":"Luis B.","affiliations":[{"id":57276,"text":"Centro de Investigación Esquel de Montaña y Estepa Patagónica (CONICET-UNPSJB), Roca 12 780, Esquel, Chubut, Argentina","active":true,"usgs":false}],"preferred":false,"id":835119,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grech, Marta G.","contributorId":279552,"corporation":false,"usgs":false,"family":"Grech","given":"Marta","email":"","middleInitial":"G.","affiliations":[{"id":57276,"text":"Centro de Investigación Esquel de Montaña y Estepa Patagónica (CONICET-UNPSJB), Roca 12 780, Esquel, Chubut, Argentina","active":true,"usgs":false}],"preferred":false,"id":835120,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Williams-Subiza, Emilio A. 0000-0001-9480-527X","orcid":"https://orcid.org/0000-0001-9480-527X","contributorId":279553,"corporation":false,"usgs":false,"family":"Williams-Subiza","given":"Emilio","email":"","middleInitial":"A.","affiliations":[{"id":57276,"text":"Centro de Investigación Esquel de Montaña y Estepa Patagónica (CONICET-UNPSJB), Roca 12 780, Esquel, Chubut, Argentina","active":true,"usgs":false}],"preferred":false,"id":835121,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stenert, Cristina","contributorId":279554,"corporation":false,"usgs":false,"family":"Stenert","given":"Cristina","affiliations":[{"id":57278,"text":"Laboratory of Ecology and Conservation of Aquatic Ecosystems, Universidade do Vale do Rio dos Sinos (UNISINOS), São Leopoldo, Brazil","active":true,"usgs":false}],"preferred":false,"id":835122,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McLean, Kyle 0000-0003-3803-0136 kmclean@usgs.gov","orcid":"https://orcid.org/0000-0003-3803-0136","contributorId":168533,"corporation":false,"usgs":true,"family":"McLean","given":"Kyle","email":"kmclean@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":835123,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Greig, Hamish S.","contributorId":279555,"corporation":false,"usgs":false,"family":"Greig","given":"Hamish S.","affiliations":[{"id":57280,"text":"University of Maine, 212 Deering Hall, Orono, ME","active":true,"usgs":false}],"preferred":false,"id":835124,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Maltchik, Leonardo 0000-0002-5321-7524","orcid":"https://orcid.org/0000-0002-5321-7524","contributorId":279556,"corporation":false,"usgs":false,"family":"Maltchik","given":"Leonardo","email":"","affiliations":[{"id":57278,"text":"Laboratory of Ecology and Conservation of Aquatic Ecosystems, Universidade do Vale do Rio dos Sinos (UNISINOS), São Leopoldo, Brazil","active":true,"usgs":false}],"preferred":false,"id":835125,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Pires, Mateus M. 0000-0002-5728-8733","orcid":"https://orcid.org/0000-0002-5728-8733","contributorId":279557,"corporation":false,"usgs":false,"family":"Pires","given":"Mateus","email":"","middleInitial":"M.","affiliations":[{"id":57278,"text":"Laboratory of Ecology and Conservation of Aquatic Ecosystems, Universidade do Vale do Rio dos Sinos (UNISINOS), São Leopoldo, Brazil","active":true,"usgs":false}],"preferred":false,"id":835126,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Bird, Matthew S.","contributorId":279558,"corporation":false,"usgs":false,"family":"Bird","given":"Matthew","email":"","middleInitial":"S.","affiliations":[{"id":57281,"text":"Department of Zoology, University of Johannesburg, Auckland Park 2006, South Africa","active":true,"usgs":false}],"preferred":false,"id":835127,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Boissezon, Aurelie","contributorId":279559,"corporation":false,"usgs":false,"family":"Boissezon","given":"Aurelie","email":"","affiliations":[{"id":57282,"text":"University of Applied Sciences and Arts Western Switzerland, HEPIA, 150 route de Presinge, CH- 1254 Jussy/ Geneva, Switzerland","active":true,"usgs":false}],"preferred":false,"id":835128,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Boix, Dani 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de Paisaje (GESAP) INIBIOMA, Universidad Nacional del Comahue, CONICET, Quintral 1250, San Carlos de Bariloche (8400), Argentina","active":true,"usgs":false}],"preferred":false,"id":835131,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Gascón, Stephanie","contributorId":279563,"corporation":false,"usgs":false,"family":"Gascón","given":"Stephanie","affiliations":[{"id":57283,"text":"GRECO, Institute of Aquatic Ecology, University of Girona, Girona, Spain","active":true,"usgs":false}],"preferred":false,"id":835132,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Jeffries, Michael","contributorId":279564,"corporation":false,"usgs":false,"family":"Jeffries","given":"Michael","email":"","affiliations":[{"id":57285,"text":"Department of Geography & Environmental Sciences, Northumbria University, Newcastle upon Tune, NE1 8ST, UK","active":true,"usgs":false}],"preferred":false,"id":835133,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Kneitel, Jamie M. 0000-0002-7841-1198","orcid":"https://orcid.org/0000-0002-7841-1198","contributorId":279565,"corporation":false,"usgs":false,"family":"Kneitel","given":"Jamie","email":"","middleInitial":"M.","affiliations":[{"id":57286,"text":"Department of Biological Sciences, California State University-Sacramento, Sacramento, CA 95819-6077, USA","active":true,"usgs":false}],"preferred":false,"id":835134,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Loskutov, Olga","contributorId":279566,"corporation":false,"usgs":false,"family":"Loskutov","given":"Olga","email":"","affiliations":[{"id":57287,"text":"Institute of Biology of Komi Scientific Centre of the Ural Branch of the Russian Academy of Sciences, 28 Kommunisticheskaya Street, 167982 Syktyvkar, Russia","active":true,"usgs":false}],"preferred":false,"id":835135,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Manzo, Luz 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Inoculation trials can quantify interactions among these players and explain aspects of disease ecology to inform management in variable and dynamic natural environments. White-nose Syndrome, a disease caused by the fungal pathogen, Pseudogymnoascus destructans (Pd), has caused severe population declines of several bat species in North America. We conducted the first experimental infection trial on the tri-colored bat, Perimyotis subflavus, to test the effect of temperature and humidity on disease severity. We also tested the effects of temperature and humidity on fungal growth and persistence on substrates. Unexpectedly, only 37% (35/95) of bats experimentally inoculated with Pd at the start of the experiment showed any infection response or disease symptoms after 83 days of captive hibernation. There was no evidence that temperature or humidity influenced infection response. Temperature had a strong effect on fungal growth on media plates, but the influence of humidity was more variable and uncertain. Designing laboratory studies to maximize research outcomes would be beneficial given the high costs of such efforts and potential for unexpected outcomes. Understanding the influence of microclimates on host–pathogen interactions remains an important consideration for managing wildlife diseases, particularly in variable environments.
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Modeling efforts to date have largely centered on predicting the effects of warming temperatures on temperate species phenology. In and near the tropics, the effects of a warming planet on species phenology are more likely to be driven by changes in the seasonal precipitation cycle rather than temperature. To demonstrate the importance of considering precipitation-driven phenology in ecological studies, we present a case study wherein we construct a mechanistic population model for a rare subtropical butterfly (Miami blue butterfly, Cyclargus thomasi bethunebakeri) and use a suite of global climate models to project butterfly populations into the future. Across all iterations of the model, the trajectory of Miami blue populations is uncertain. 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Center","active":true,"usgs":true}],"preferred":true,"id":853985,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gellis, Allen C. 0000-0002-3449-2889 agellis@usgs.gov","orcid":"https://orcid.org/0000-0002-3449-2889","contributorId":197684,"corporation":false,"usgs":true,"family":"Gellis","given":"Allen","email":"agellis@usgs.gov","middleInitial":"C.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true}],"preferred":true,"id":853986,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fuller, Christopher C. 0000-0002-2354-8074 ccfuller@usgs.gov","orcid":"https://orcid.org/0000-0002-2354-8074","contributorId":1831,"corporation":false,"usgs":true,"family":"Fuller","given":"Christopher","email":"ccfuller@usgs.gov","middleInitial":"C.","affiliations":[{"id":374,"text":"Maryland Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":853987,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Schmidt, Travis S. 0000-0003-1400-0637 tschmidt@usgs.gov","orcid":"https://orcid.org/0000-0003-1400-0637","contributorId":1300,"corporation":false,"usgs":true,"family":"Schmidt","given":"Travis S.","email":"tschmidt@usgs.gov","affiliations":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":853988,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70230062,"text":"70230062 - 2022 - Soil moisture response to seasonal drought conditions and post-thinning forest structure","interactions":[],"lastModifiedDate":"2022-08-01T16:55:02.409081","indexId":"70230062","displayToPublicDate":"2022-01-19T06:17:05","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1447,"text":"Ecohydrology","active":true,"publicationSubtype":{"id":10}},"title":"Soil moisture response to seasonal drought conditions and post-thinning forest structure","docAbstract":"Prolonged drought conditions in semi-arid forests can lead to widespread vegetation stress and mortality. However, the distribution of these effects is not spatially uniform. We measured soil water potential at high spatial and temporal resolution using 112 sensors distributed across a ponderosa pine forest in northern Arizona, USA, during two abnormally dry years with below-average total precipitation. We used the data to assess the effects of fore-summer drought period on the timing, magnitude, and extent of drying throughout the top 100 cm of the soil profile. Additionally, we use high spatial resolution terrestrial lidar measurements of forest structure to develop relationships between soil drying and fine-scale forest structure. We find that increasing drought from 2019 to 2020 caused significantly earlier onset of soil dying at all depths (25, 50 and 100 cm) and more days below a critical drying threshold for ponderosa pine. Additionally, our results show that significantly drier soils are found in areas with higher stand-level basal area, canopy cover and tree density, and shorter trees. Our results from the unprecedented spatial and temporal resolution data suggest that tailored restoration thinning with specific tree density and size parameters can be used to increase and prolong the availability of deep soil water to trees during drought.
Managers rely on accurate estimators of wildlife abundance and trends for management decisions. Despite the focus of contemporary wildlife science on developing methods to improve inference from wildlife surveys, legacy datasets often rely on index counts that lack information about the detection process. Data integration can be a useful tool for combining index counts with data collected under more rigorous designs (i.e., designs that account for the detection process), but care is required when datasets represent different population processes or are mismatched in space and time. This can be particularly problematic in cases where animals aggregate in response to a spatially or temporally limited resource because individuals may temporarily immigrate from outside the study area and be included in the abundance index. Abundance indices based on brown bear (Ursus arctos) feeding aggregations within coastal meadows in early summer in Lake Clark National Park and Preserve, Alaska, USA, are one such example. These indices reflect the target population (brown bears residing within the park) and temporary immigrants (i.e., bears drawn from outside the park boundary). To properly account for the effects of temporary immigration, we integrated the index data with abundance data collected via park-wide distance sampling surveys, the latter of which properly addressed the detection process. By assuming that the distance data provide inference on abundance and the index counts represent some combination of abundance and temporary immigration processes, we were able to decompose the relative contribution of each to overall trend. We estimated that the density of brown bears within our study area was 38–54 adults/1,000 km2 during 2003–2019 and that abundance increased at a rate of approximately 1.4%/year. The contribution of temporary immigrants to overall trend in the index was low, so we created 3 hypothetical scenarios to more fully demonstrate how the integrated approach could be useful in situations where the composite trend in meadow counts may obscure trends in abundance (e.g., opposing trends in abundance and temporary immigration). Our work represents a conceptual advance supporting the integration of legacy index data with more rigorous data streams and is broadly applicable in cases where trends in index values may represent a mixture of population processes.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22185","usgsCitation":"Schmidt, J., Wilson, T.L., Thompson, W., and Mangipane, B., 2022, Integrating distance sampling survey data with population indices to separate trends in abundance and temporary immigration: Journal of Wildlife Management, v. 86, no. 3, e22185, 15 p., https://doi.org/10.1002/jwmg.22185.","productDescription":"e22185, 15 p.","ipdsId":"IP-130320","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":480766,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Lake Clark National Park and Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -154.33642551397307,\n 61.82002560280111\n ],\n [\n -154.33642551397307,\n 60.53613297738664\n ],\n [\n -151.8343884908579,\n 60.53613297738664\n ],\n [\n -151.8343884908579,\n 61.82002560280111\n ],\n [\n -154.33642551397307,\n 61.82002560280111\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"86","issue":"3","noUsgsAuthors":false,"publicationDate":"2022-01-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Schmidt, Joshua H.","contributorId":349537,"corporation":false,"usgs":false,"family":"Schmidt","given":"Joshua H.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":924368,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilson, Tammy L. 0000-0002-3672-8277","orcid":"https://orcid.org/0000-0002-3672-8277","contributorId":293684,"corporation":false,"usgs":true,"family":"Wilson","given":"Tammy","email":"","middleInitial":"L.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":924367,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thompson, William L.","contributorId":349538,"corporation":false,"usgs":false,"family":"Thompson","given":"William L.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":924369,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mangipane, Buck A.","contributorId":349540,"corporation":false,"usgs":false,"family":"Mangipane","given":"Buck A.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":924370,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227434,"text":"dr1148 - 2022 - Distribution and abundance of Least Bell’s Vireos (Vireo bellii pusillus) and Southwestern Willow Flycatchers (Empidonax traillii extimus) at the San Antonio Dam, Los Angeles and San Bernardino Counties, California—2021 Data summary","interactions":[],"lastModifiedDate":"2022-01-19T12:22:31.582508","indexId":"dr1148","displayToPublicDate":"2022-01-18T14:45:43","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":9318,"text":"Data Report","code":"DR","onlineIssn":"2771-9448","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1148","displayTitle":"Distribution and Abundance of Least Bell’s Vireos (Vireo bellii pusillus) and Southwestern Willow Flycatchers (Empidonax traillii extimus) at the San Antonio Dam, Los Angeles and San Bernardino Counties, California—2021 Data Summary","title":"Distribution and abundance of Least Bell’s Vireos (Vireo bellii pusillus) and Southwestern Willow Flycatchers (Empidonax traillii extimus) at the San Antonio Dam, Los Angeles and San Bernardino Counties, California—2021 Data summary","docAbstract":"We surveyed for Least Bell’s Vireos (Vireo bellii pusillus; vireo) and Southwestern Willow Flycatchers (Empidonax traillii extimus; flycatcher) at the San Antonio Dam near Upland, California, in 2021. Four vireo surveys were conducted between April 16 and July 15, 2021, and three flycatcher surveys were conducted between May 27 and July 15, 2021.
We detected one transient vireo and one transient flycatcher. No territorial vireos or flycatchers were observed. The vireo was found in riparian scrub habitat dominated by native mule fat (Baccharis salicifolia), whereas the flycatcher was using habitat dominated by non-native tamarisk (Tamarix ramosissima).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1148","programNote":"Ecosystems Mission Area-Species Management Research Program","usgsCitation":"Howell, S.L., and Kus, B.E., 2022, Distribution and abundance of Least Bell’s Vireos (Vireo bellii pusillus) and Southwestern Willow Flycatchers (Empidonax traillii extimus) at the San Antonio Dam, Los Angeles and San Bernardino Counties, California—2021 Data summary: U.S. Geological Survey Data Report 1148, 8 p., https://doi.org/10.3133/dr1148.","productDescription":"vii, 8 p.","numberOfPages":"8","onlineOnly":"Y","ipdsId":"IP-135056","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":394401,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1148/images"},{"id":394400,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1148/dr1148.xml"},{"id":394398,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1148/covrthb.jpg"},{"id":394399,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1148/dr1148.pdf","text":"Report","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"California","county":"Los Angeles County, San Bernardino County","otherGeospatial":"San Antonio Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.76039123535155,\n 34.12942636161218\n ],\n [\n -117.56744384765625,\n 34.12942636161218\n ],\n [\n -117.56744384765625,\n 34.231673921638475\n ],\n [\n -117.76039123535155,\n 34.231673921638475\n ],\n [\n -117.76039123535155,\n 34.12942636161218\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Lake St. Clair Metropark Beach (LSCMB) in Michigan is a public beach near the mouth of the Clinton River that has a history of beach closures for public health concerns. The Clinton River is designated as a Great Lakes Area of Concern, and the park has a Beneficial Use Impairment for beach closings because of elevated Escherichia coli (E. coli) concentrations. The U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency and in collaboration with the Michigan Department of the Environment, Great Lakes, and Energy, Macomb County Health Department, and Huron-Clinton Metroparks, completed a 2-year study to determine sources of E. coli in LSCMB. Samples were collected during dry and wet weather periods to observe the sampling sites under different conditions. Nearshore surface water samples were collected biweekly July through October in 2018 and May through September in 2019. There were 20 sampling sites along the shoreline of the park and in the channel north of the park. In addition to collecting nearshore surface-water samples, samples were collected from shallow groundwater, lake-bottom material, standing water on the beach and surrounding the recreational beach area, solids (beach sands and detritus), and offshore surface-water sites. In 2019, additional samples for microbial source tracking (MST) were collected on three dates in midsummer and were analyzed for human (HF183) and bird/waterfowl (GFD) MST markers. The concentrations of E. coli at LSCMB (in order of highest to lowest E. coli concentrations) were as follows: shallow groundwater nearest to the water’s edge, surface sands and organic matter (detritus), standing water on the beach, nearshore surface water in and surrounding the recreational beach area, lake-bottom material, and offshore surface water. The combination of low E. coli concentrations offshore and higher concentrations nearshore indicate nearshore sources, possibly from beach sands or groundwater, rather than sources coming from offshore Lake St. Clair waters. The subset of samples for MST analysis did not have enough positive results to illustrate MST trends, but this study demonstrated that both human and waterfowl sources can affect the water quality at LCSMB.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215089","collaboration":"Prepared in cooperation with the U.S. Environmental Protection Agency","usgsCitation":"Fogarty, L.R., Maurer, J.A., Hyslop, I.M., Totten, A.R., Kephart, C.M., and Brennan, A.K., 2021, Understanding sources and distribution of Escherichia coli at Lake St. Clair Metropark Beach, Macomb County, Michigan: U.S. Geological Survey Scientific Investigations Report 2021–5089, 34 p., https://doi.org/10.3133/sir20215089.","productDescription":"Report: ix, 34 p.; Dataset","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-125120","costCenters":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true},{"id":35860,"text":"Ohio-Kentucky-Indiana Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":502112,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_112127.htm","linkFileType":{"id":5,"text":"html"}},{"id":394369,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5089/images"},{"id":394366,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5089/coverthb.jpg"},{"id":394367,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5089/sir20215089.pdf","text":"Report","size":"8.55 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5089"},{"id":394368,"rank":3,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"U.S. Geological Survey National Water Information System database","description":"USGS Dataset","linkHelpText":"— USGS water data for the Nation"}],"country":"United States","state":"Michigan","county":"Macomb County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-83.1025,42.8884],[-82.9839,42.8939],[-82.8674,42.8958],[-82.7384,42.8967],[-82.7276,42.6807],[-82.7366,42.6755],[-82.7474,42.6731],[-82.7578,42.6656],[-82.7593,42.6611],[-82.7593,42.6598],[-82.7594,42.6589],[-82.7765,42.6544],[-82.7901,42.6552],[-82.8002,42.6541],[-82.8093,42.6466],[-82.8168,42.635],[-82.8176,42.6323],[-82.82,42.6215],[-82.8178,42.616],[-82.8119,42.6103],[-82.7989,42.6081],[-82.7941,42.6053],[-82.7906,42.5997],[-82.7901,42.5983],[-82.7765,42.5957],[-82.7741,42.5933],[-82.7774,42.5912],[-82.7837,42.5891],[-82.7853,42.5823],[-82.7822,42.5708],[-82.7843,42.5672],[-82.785,42.5654],[-82.7874,42.5664],[-82.7904,42.5692],[-82.7984,42.5717],[-82.8139,42.5717],[-82.8252,42.5702],[-82.8348,42.5659],[-82.8458,42.559],[-82.849,42.5563],[-82.848,42.5518],[-82.8525,42.5487],[-82.8551,42.547],[-82.8623,42.5408],[-82.871,42.5288],[-82.873,42.5261],[-82.8771,42.5194],[-82.8829,42.5027],[-82.8824,42.4886],[-82.8831,42.4873],[-82.8832,42.485],[-82.884,42.4823],[-82.8836,42.4786],[-82.8831,42.4759],[-82.8827,42.4713],[-82.8727,42.4611],[-82.8687,42.4546],[-82.8687,42.4537],[-82.9691,42.4492],[-83.0843,42.4463],[-83.0867,42.5355],[-83.0905,42.6238],[-83.0986,42.801],[-83.1025,42.8884]]]},\"properties\":{\"name\":\"Macomb\",\"state\":\"MI\"}}]}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
5840 Enterprise Drive
Lansing, MI 48911
Advances in global lightning detection have provided novel ways to characterize explosive volcanism. However, researchers are still at the early stages of understanding how volcanic plumes become electrified on different spatial and temporal scales. We deconstructed the phreatomagmatic eruption of Taal volcano (Philippines) on 12 January 2020 to investigate the origin of its powerful volcanic thunderstorm. Satellite analysis indicated that the water-rich plume rose >10 km high before creating lightning detected by Vaisala's global lightning data set (GLD360). Flash rates increased with plume heights and cloud expansion over time, producing >70 flashes min–1. Photographs revealed a highly electrified region at the base of the umbrella cloud, where we infer strong convective updrafts and icy collisions enhanced the electrical activity. These findings inform a conceptual model with overlapping regimes of charge generation in wet eruptions—initially due to ash particle collisions near the vent, followed by thunderstorm-like electrification in icy regions of the upper plume. Despite the wide reach of Taal's ash cloud, most of the lightning occurred within 20–30 km of the volcano, producing thousands of hazardous cloud-to-ground flashes over a densely populated area. The eruption demonstrates that volcanic lightning can pose a hazard in its own right, embedded within the broader hazards of explosive volcanism in an urban setting.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/G49490.1","usgsCitation":"Van Eaton, A.R., Smith, C.M., Pavolonis, M.J., and Said, R., 2022, Eruption dynamics leading to a volcanic thunderstorm— The January 2020 eruption of Taal volcano, Philippines: Geology, v. 50, no. 4, p. 491-495, https://doi.org/10.1130/G49490.1.","productDescription":"5 p.","startPage":"491","endPage":"495","ipdsId":"IP-128543","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467203,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1130/geol.s.17265287","text":"External Repository"},{"id":466649,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Philippines","otherGeospatial":"Taal volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 120.90268362290232,\n 14.102133334852596\n ],\n [\n 120.90268362290232,\n 13.86909521479896\n ],\n [\n 121.11807482441446,\n 13.86909521479896\n ],\n [\n 121.11807482441446,\n 14.102133334852596\n ],\n [\n 120.90268362290232,\n 14.102133334852596\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"50","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-01-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Van Eaton, Alexa R. 0000-0001-6646-4594 avaneaton@usgs.gov","orcid":"https://orcid.org/0000-0001-6646-4594","contributorId":184079,"corporation":false,"usgs":true,"family":"Van Eaton","given":"Alexa","email":"avaneaton@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":924036,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Cassandra M","contributorId":257012,"corporation":false,"usgs":false,"family":"Smith","given":"Cassandra","email":"","middleInitial":"M","affiliations":[{"id":7163,"text":"University of South Florida","active":true,"usgs":false}],"preferred":false,"id":924037,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pavolonis, Michael J.","contributorId":199297,"corporation":false,"usgs":false,"family":"Pavolonis","given":"Michael","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":924038,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Said, Ryan 0000-0002-8095-4204","orcid":"https://orcid.org/0000-0002-8095-4204","contributorId":257003,"corporation":false,"usgs":false,"family":"Said","given":"Ryan","email":"","affiliations":[{"id":51953,"text":"Vaisala, Inc.","active":true,"usgs":false}],"preferred":false,"id":924039,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70229710,"text":"70229710 - 2022 - No evidence for tephra in Greenland from the historic eruption of Vesuvius in 79 CE: Implications for geochronology and paleoclimatology","interactions":[],"lastModifiedDate":"2022-03-16T14:50:49.888833","indexId":"70229710","displayToPublicDate":"2022-01-18T09:44:42","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1250,"text":"Climate of the Past","active":true,"publicationSubtype":{"id":10}},"title":"No evidence for tephra in Greenland from the historic eruption of Vesuvius in 79 CE: Implications for geochronology and paleoclimatology","docAbstract":"Volcanic fallout in polar ice sheets provides important opportunities to date and correlate ice-core records as well as to investigate the environmental impacts of eruptions. Only the geochemical characterization of volcanic ash (tephra) embedded in the ice strata can confirm the source of the eruption, however, and is a requisite if historical eruption ages are to be used as valid chronological checks on annual ice layer counting. Here we report the investigation of ash particles in a Greenland ice core that are associated with a volcanic sulfuric acid layer previously attributed to the 79 CE eruption of Vesuvius. Major and trace element composition of the particles indicates that the tephra does not derive from Vesuvius but most likely originates from an unidentified eruption in the Aleutian arc. Using ash dispersal modeling, we find that only an eruption large enough to include stratospheric injection is likely to account for the sizable (24–85 µm) ash particles observed in the Greenland ice at this time. Despite its likely explosivity, this event does not appear to have triggered significant climate perturbations, unlike some other large extratropical eruptions. In light of a recent re-evaluation of the Greenland ice-core chronologies, our findings further challenge the previous assignation of this volcanic event to 79 CE. We highlight the need for the revised Common Era ice-core chronology to be formally accepted by the wider ice-core and climate modeling communities in order to ensure robust age linkages to precisely dated historical and paleoclimate proxy records.
","language":"English","publisher":"Copernicus Publications","doi":"10.5194/cp-18-45-2022","usgsCitation":"Plunkett, G., Sigl, M., Schwaiger, H., Tomlinson, E., Toohey, M., McConnell, J.R., Pilcher, J.R., Hasegawa, T., and Siebe, C., 2022, No evidence for tephra in Greenland from the historic eruption of Vesuvius in 79 CE: Implications for geochronology and paleoclimatology: Climate of the Past, v. 18, no. 1, p. 45-65, https://doi.org/10.5194/cp-18-45-2022.","productDescription":"21 p.","startPage":"45","endPage":"65","ipdsId":"IP-129942","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":449118,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/cp-18-45-2022","text":"Publisher Index Page"},{"id":397152,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Greenland","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n 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Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5119","displayTitle":"Characterization of Ambient Groundwater Quality Within a Statewide, Fixed-Station Monitoring Network in Pennsylvania, 2015–19","title":"Characterization of ambient groundwater quality within a statewide, fixed-station monitoring network in Pennsylvania, 2015–19","docAbstract":"Pennsylvania leads the Nation in the number of individuals that use groundwater for private domestic water supply; more than 3 million rural and suburban Pennsylvania residents rely on private domestic supplies for drinking water. These supplies are not regulated nor routinely monitored; thus relevant groundwater-quality information is not widely available. The U.S. Geological Survey (USGS), in cooperation with the Pennsylvania Department of Environmental Protection (PaDEP) Safe Drinking Water Bureau, established a statewide, fixed-station ambient groundwater quality network in 2015. The goals for the Pennsylvania Groundwater Monitoring Network (GWMN) include characterizing ambient groundwater quality conditions in rural areas of the State and documenting potential changes in conditions over time. Seventeen wells were selected for monitoring at 6-month intervals beginning in 2015. Since then, several wells have been added to the GWMN, bringing the total number of wells sampled in the fall of 2019 to 28. Routinely monitored constituents included physical characteristics and chemical concentrations in filtered and unfiltered samples (major and trace elements, nutrients, and organic compounds). Samples for volatile organic compounds (VOCs), radionuclides, and dissolved hydrocarbon gases were collected during the first sampling event at each well.
To offer insights on the quality of groundwater used for domestic supply in Pennsylvania, summary statistics for the 221 GWMN samples collected during 2015–19 are compared to U.S. Environmental Protection Agency (EPA) drinking-water standards, which are applicable to public water supplies. Results show that samples across the GWMN generally meet drinking-water standards for inorganic and organic constituents; however, a percentage of samples had concentrations that exceeded maximum contaminant level (MCL) thresholds for nitrate (3 percent) and secondary maximum contaminant level (SMCL) thresholds for iron (32 percent), manganese (36 percent), and aluminum (5 percent). Radon-222 activities, which were sampled only during the initial visit to a well, exceeded the lower proposed drinking water standard of 300 picocuries per liter (pCi/L) in 64 percent of wells in the GWMN; additionally, 7 percent of wells exceeded the higher proposed standard of 4,000 pCi/L. There were no exceedances for VOCs, but one well had a tribromomethane detection. Three wells had detectable concentrations of methane, with one sample exceeding the Pennsylvania action level of 7 milligrams per liter (mg/L).
The pH and dissolved oxygen concentrations varied widely across the GWMN and were correlated with dissolved metal concentrations and other chemical characteristics of groundwater samples. Considering all samples collected for the study, the pH ranged from 4.2 to 8.3; 42 percent of pH values were either above or below the SMCL range of 6.5–8.5. The highest pH values resulted from contamination of loose grout used in the construction of one well and decreased to levels consistent with other wells in the vicinity after repeated sampling rounds. Dissolved oxygen (DO), which ranged from 0 to 13.9 mg/L, influences the mobility and prevalence of constituents with variable oxidation state, including iron, manganese, and nitrogen species. Samples with acidic pH (less than 6.5) and (or) low DO had the highest concentrations of manganese and iron, whereas those with neutral to alkaline pH values had the highest concentrations of calcium, magnesium, sodium, and other major ions. Analysis of major ions indicates that calcium/bicarbonate water types are the most common, with a few characterized as calcium/chloride or sodium/chloride, and most others as mixed water types including calcium-magnesium/bicarbonate, sodium-magnesium/bicarbonate, and sodium/bicarbonate-chloride.
Nonparametric statistical methods were used to evaluate the data for spatial and temporal trends. A principal components analysis (PCA) model developed with ranked data values for the entire network resulted in three components, (1) dissolved solids, (2) redox, and (3) sodium-chloride, which explained 74.5 percent of variance in the dataset. On the basis of individual contributions to the PCA, certain wells were identified through hierarchical cluster analysis that shared relevant water-quality characteristics. The spatial distribution of sampling locations and the temporal trends of constituent concentrations indicate that hydrogeologic setting and topographic position as defined in the PCA model are important factors affecting the spatial and temporal patterns of groundwater quality in the GWMN.
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\"}}]}","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
Underground infiltration basins (UIBs) mimic the natural hydrologic cycle by allowing stormwater to recharge local groundwater aquifers. However, little is known about the potential transport of organic contaminants to receiving groundwater. We conducted a pilot study in which we collected paired grab samples of stormwater runoff flowing into two UIBs (inflow) and shallow groundwater adjacent to the UIBs. Samples were collected coincident with three rain events and analyzed for volatile organic compounds, semi-volatile organic compounds, pharmaceuticals, and pesticides. Few contaminants were detected in groundwater, compared with inflow, and groundwater concentrations were typically an order of magnitude less. With one exception (trichloroethene), all groundwater concentrations were at least two orders of magnitude below available guidance or screening values. This short communication highlights information gaps in understanding the hydrologic connectivity between UIBs and receiving groundwater and potential consequent contaminant transport to the subsurface from varying climatic conditions.
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