Fire transforms, fragments and sometimes maintains forests, creating mosaics of burned and unburned patches. Highly mobile animals respond to resources in the landscape at a variety of spatial scales, yet we know little about their landscape-scale relationships with fire.
We aimed to identify drivers of bat richness in a landscape mosaic of forested and burned areas while identifying spatial scales at which bat richness was most strongly related to extent, configuration, and diversity measures of landscape-level habitat.
We used multi-species hierarchical occupancy modelling to relate bat richness to landscape variables at 10 spatial scales, based on acoustic data collected in the Sierra Nevada, United States. We also assessed redundancy among landscape variable type (extent, configuration, and diversity) and between focal patch types (forested and burned).
Bat richness was positively associated with heterogenous landscapes, shown by positive associations with pyrodiversity, extent and mean area of burned patches, burned and forested edge density and patch density and relationships were generally consistent across scales. Extent of forest cover and burned areas were highly correlated, but configuration and diversity of these patch types diverged.
Bat communities of our study area appear to be largely resilient to wildfire and adapted to more heterogenous forests and shorter-interval fire regimes that likely predominated before the fire suppression era.
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Theory and empirical precedent indicate that a primary benefit of many territories is exclusive access to food resources, and primary costs of defending and using space are associated with competition, travel and mortality risk. A recently developed mechanistic model for economical territory selection provided numerous empirically testable predictions. We tested these predictions using location data from grey wolves (Canis lupus) in Montana, USA. As predicted, territories were smaller in areas with greater densities of prey, competitors and low-use roads, and for groups of greater size. Territory size increased before decreasing curvilinearly with greater terrain ruggedness and harvest mortalities. Our study provides evidence for the economical selection of territories as a causal mechanism underlying ecological patterns observed in a cooperative carnivore. Results demonstrate how a wide range of environmental and social conditions will influence economical behaviour and resulting space use. We expect similar responses would be observed in numerous territorial species. A mechanistic approach enables understanding how and why animals select particular territories. This knowledge can be used to enhance conservation efforts and more successfully predict effects of conservation actions.
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Water Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 432
Reston, VA 20192
Repeat microgravity surveys carried out using relative- and absolute-gravity meters are useful for identifying changes in subsurface mass, such as the volume of water stored in an aquifer. These surveys require careful field procedures to achieve the part-per-billion accuracy required to measure the small changes in gravity relevant for hydrologic studies. This chapter describes techniques and methods for carrying out gravity surveys, requirements for assuring high-quality survey results, and data processing and archival procedures. The focus is on acquiring and documenting repeat gravity surveys for monitoring changes in groundwater storage. Similar gravity surveys may be completed to evaluate other causes of mass change, such as those caused by magma movement below volcanoes. The methods are also useful for one-time surveys that map spatial gravity variations associated with geologic structures such as faults or sedimentary basins.
Repeat microgravity surveys can be carried out using relative-gravity meters, absolute-gravity meters, or both. Specific locations, known as gravity stations, are visited during each survey. Most commonly, absolute- and relative-gravity are combined using the least-squares method of network adjustment, much like benchmark elevations and relative-height differences in a leveling network. This chapter primarily describes the use of the A-10 absolute-gravity meter manufactured by Micro-g LaCoste, Inc., and relative-gravity meters made by LaCoste & Romberg (no longer in production) and ZLS Corporation, Inc. Field and office procedures are similar for other instruments such as the FG-5 absolute-gravity meter and Scintrex relative-gravity meters, but some adaptation may be required. Quality control for absolute-gravity data focuses primarily on proper field procedures and maintaining the time and distance calibration of the instrument. Quality control for relative-gravity surveys requires careful field procedures, an understanding of how the meter is behaving while in the field, and appropriate postprocessing.
The techniques and methods described in this chapter were developed over 30 years at the USGS Arizona Water Science Center and the Southwest Gravity Program and are the basis for many studies on groundwater-storage change and geologic structure. A description of the Program and complete bibliography is available at https://www.usgs.gov/centers/az-water/science/azwsc-capabilities-hydrologic-gravity-monitoring.
Director,
Arizona Water Science Center
U.S. Geological Survey
520 N. Park Avenue
Tucson, AZ 85719
Aerial surveys integrating probability-based sample designs have been implemented successfully to estimate relative abundance of wintering ducks in Arkansas, Louisiana, Mississippi, and Missouri, but these approaches have not been evaluated in the Atlantic Flyway except for American black ducks (Anas rubripes) along the Atlantic coast. Furthermore, these surveys have not been used to index abundance of other nonbreeding waterbirds. Given elimination or reduction of resources allocated to the Midwinter Waterfowl Survey in the Atlantic Flyway and elsewhere, the South Carolina Department of Natural Resources (SCDNR) expressed a need for reliable surveys to monitor waterfowl and other waterbirds during autumn through winter. We designed stratified aerial strip-transect surveys to estimate population indices for migrating and wintering dabbling ducks (Anatini), diving ducks (Aythini, Mergini, Oxyurini), pelagic and piscivorous waterbirds (Anhingidae, Laridae, Pelicanidae, Phalacrocoracidae), and wading birds (Ardeidae, Ciconiidae, Threskiornithidae) in coastal and inland regions of South Carolina during autumn-winter 2017–2019. We used unequal probability random sampling to estimate population indices with deemed adequate precision (i.e., coefficient of variation [CV] ≤ 20%) and estimated theoretical survey efforts needed to achieve desired precision for future aerial surveys. Indices met our goal for precision in September and January 2018 for wading birds, in February and November 2018 for pelagic waterbirds, and in February 2018 for diving ducks, but never for other ducks during South Carolina waterfowl hunting season. We detected peak abundance of dabbling and diving ducks in January and wading birds and wood storks (Mycteria americana) in September. We estimated ~2.5 times greater survey effort was needed across waterbird taxa than was expended to achieve a CV=20%. We also used survey data to depict spatiotemporal variation in waterbird distributions across the study area. Our surveys are applicable for the SCDNR and other agencies seeking to monitor autumn-winter waterbird populations. Although survey refinements are necessary to increase precision in South Carolina, our waterbird indices are useful to assess population trends through time, guide habitat management and restoration efforts, refine local harvest regulations, inform law enforcement to detected illicit activities (e.g., baiting), and monitor possible shifting waterbird distributions in response to land-use and climate change.
","language":"English","publisher":"Southeastern Association of Fish and Wildlife Agencies","usgsCitation":"Ross, B., Wilkerson, G., Kneece, M., Masto, N., Gerard, P., and Kaminski, R., 2021, Aerial strip-transect surveys: Indexing autumn–winter waterbird abundance and distribution in South Carolina: Journal of the Southeastern Association of Fish and Wildlife Agencies, v. 8, p. 89-100.","productDescription":"12 p.","startPage":"89","endPage":"100","ipdsId":"IP-119300","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":480972,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://seafwa.org/journal/2021/aerial-strip-transect-surveys-indexing-autumn-winter-waterbird-abundance-and"},{"id":481109,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"South 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Kennedy Waterfowl & Wetlands Conservation Center","active":true,"usgs":false}],"preferred":false,"id":924830,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70218461,"text":"ds1133 - 2021 - Compilation of information on occurrence and conservation status for the freshwater mussel fauna of Nebraska, Kansas, and Oklahoma","interactions":[],"lastModifiedDate":"2022-07-12T12:14:55.286929","indexId":"ds1133","displayToPublicDate":"2021-03-01T13:00:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1133","displayTitle":"Compilation of Information on Occurrence and Conservation Status for the Freshwater Mussel Fauna of Nebraska, Kansas, and Oklahoma","title":"Compilation of information on occurrence and conservation status for the freshwater mussel fauna of Nebraska, Kansas, and Oklahoma","docAbstract":"The purpose of this data series is to compile information on the occurrence and conservation status of the freshwater mussel fauna of Nebraska, Kansas, and Oklahoma and to map the distribution of a freshwater mussel assemblage for the U.S. Department of the Interior, Bureau of Land Management Rapid Ecoregional Assessment (REA) program. The six focal species in the freshwater mussel assemblage are Amblema plicata (threeridge), Fusconaia flava (Wabash pigtoe), Lampsilis cardium (plain pocketbook), Lampsilis teres (yellow sandshell), Pyganodon grandis (giant floater), and Uniomerus tetralasmus (pondhorn). The focal species were selected using the following criteria: (1) the species are regionally significant, (2) occurrence records are sufficient to map the distribution of the species by hydrologic subbasins, (3) the assemblage includes species representing a range of State-level conservation priorities, and (4) the species are not listed as federally endangered or threatened. In addition, the species represent a broad array of life history strategies and habitat associations.
A total of 61 native species of freshwater mussels have documented occurrences within at least 1 of the 3 States, including 6 species that appear to have been extirpated from all the States and 6 species that may have been extirpated from at least 1 State. Of the 61 species, 8 species (including 3 potentially extirpated species) are listed as federally threatened or endangered and an additional 5 species are ranked as imperiled or vulnerable across their range. Approximately 80 percent of the native species known to have occurred within the three-State area have a secure conservation status, in comparison to only 40 percent of all freshwater mussel species or subspecies occurring within the United States. The compiled records for the contemporary period (1970–2017) documented the occurrence of 24 extant species in Nebraska, 42 in Kansas, and 48 in Oklahoma.
The contemporary distributions of the six focal species were mapped by subbasins and the larger hydrologic subregions. Historical records (prior to 1962) were also mapped but were limited. Amblema plicata, Fusconaia flava, and Lampsilis cardium were present in approximately one-third of all subbasins and slightly more than half of the subregions, primarily along the eastern portion of the three-State area. Lampsilis teres and Uniomerus tetralasmus were more widespread, occurring in close to half of the subbasins and about three-quarters of the subregions. Pyganodon grandis was the most widespread, occurring in about three-quarters of the subbasins and almost all subregions. There were very few subbasins with historical occurrences that lacked contemporary occurrences. The broad-scale distribution maps for the freshwater mussel assemblage presented with this report are intended to contribute baseline information for regional assessments, such as the Southern Great Plains Rapid Ecoregional Assessment. Despite the limitations of the available data, such baseline information can be useful for identifying data gaps, monitoring future trends, identifying conservation priorities, and providing the larger context for more detailed watershed- or catchment-level studies. ScienceBase data release files associated with this data series are available at https://doi.org/10.5066/P9SBFZJU (Fancher and Carr, 2021).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1133","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Carr, N.B., and Fancher, T.S., 2021, Compilation of information on occurrence and conservation status for the freshwater mussel fauna of Nebraska, Kansas, and Oklahoma: U.S. Geological Survey Data Series 1133, 22 p., https://doi.org/10.3133/ds1133.","productDescription":"Report: vi, 22 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-119647","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":383657,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1133/ds1133.pdf","text":"Report","size":"13.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1133"},{"id":383666,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SBFZJU","text":"USGS data 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\"}}]}","contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Building C
Fort Collins, CO 80526-8118
USA: CALIFORNIA: Orange Co.: San Juan Capistrano (33.51°N,117.66°W; WGS 84). 25 August 2020. Samuel Fisher, Chelsea Martin, Robert Fisher. Verified by Gregory B. Pauly. Natural History Museum of Los Angeles County (LACM 191974). New county record. One juvenile (33 mm SVL) was collected, and another juvenile was seen 40 m away. Another juvenile was also observed during a second visit to the site on 19 September 2020. Wall geckos were first documented at this site since at least 2019 by Gary Nafis (G. Nafis, pers. comm. and as posted on www.californiaherps.com; 23 Aug 2020). Given that multiple individuals were observed at this site over 2 mo, this appears to be an established population. Invasive T. annularis were first detected in California in Redlands, San Bernardino County in the early 2000s (Wilcox et al. 2014. Herpetol. Rev. 45:464). This new Orange County population is ca. 80 km SW of the other known California population. While T. annularis has not spread much in the downtown urban center in Redlands over the last 20 years (S. Fisher, unpubl. data), it is possible it might expand its range more rapidly in a less urbanized habitat if there were more landscaping and natural features present. The only other published records for North America are from Florida where they are also invasive, and they have been known since the 1990s from several locations and continued to spread (Krysko et al. 2016. IRCF Rept. Amphib. 23:110-143). In the native range of T. annularis their habitat consists of desert, indicating that even though they are able to breed and persist in coastal Orange County they may not be in the optimal habitat (Ibrahim 2004. Zool. Middle East 31:23–38). A potential concern is that if T. annularis becomes more widespread in Southern California, they could present a risk to endemic nocturnal rock-dwelling species such as Xantusia henshawi and Phyllodactylus nocticolus because T. annularis has been shown to engage in saurophagy (Ibrahim 2004, op. cit.). It is much larger than these species (Xantusia henshawi SVL = 70 mm; Phyllodactylus nocticolus SVL = 63 mm; Tarentola SVL = 108 mm) and well adapted to desert habitats where it could be a potential predator or competitor.
","language":"English","publisher":"Society for the Study of Amphibians and Reptiles","usgsCitation":"Fisher, S., Martin, C.E., and Fisher, R.N., 2021, Tarentola annularis (white-spotted wall gecko): Herpetological Review, v. 52, no. 1.","productDescription":"1 p.","startPage":"85","ipdsId":"IP-128505","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":389481,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Orange County","city":"San Juan Capistrano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.67378807067873,\n 33.49330726228523\n ],\n [\n -117.65027046203613,\n 33.49330726228523\n ],\n [\n -117.65027046203613,\n 33.51477815748719\n ],\n [\n -117.67378807067873,\n 33.51477815748719\n ],\n [\n -117.67378807067873,\n 33.49330726228523\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"52","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fisher, Samuel R","contributorId":225265,"corporation":false,"usgs":false,"family":"Fisher","given":"Samuel R","affiliations":[{"id":41086,"text":"La Sierra University","active":true,"usgs":false}],"preferred":false,"id":816868,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Martin, Chelsea E","contributorId":259234,"corporation":false,"usgs":false,"family":"Martin","given":"Chelsea","email":"","middleInitial":"E","affiliations":[{"id":52330,"text":"Loma Linda University","active":true,"usgs":false}],"preferred":false,"id":816869,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fisher, Robert N. 0000-0002-2956-3240 rfisher@usgs.gov","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":1529,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert","email":"rfisher@usgs.gov","middleInitial":"N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":816870,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70226917,"text":"70226917 - 2021 - Cloud water interception in Hawai‘i: Developing capacity to characterize the spatial patterns and effects on water and ecological processes responses in Hawai‘i","interactions":[],"lastModifiedDate":"2021-12-21T15:26:52.456702","indexId":"70226917","displayToPublicDate":"2021-03-01T09:21:03","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":9958,"text":"Final Technical Report","active":true,"publicationSubtype":{"id":1}},"title":"Cloud water interception in Hawai‘i: Developing capacity to characterize the spatial patterns and effects on water and ecological processes responses in Hawai‘i","docAbstract":"Cloud-water interception (CWI) is the process by which fog or cloud water droplets are captured and accumulate on the leaves and branches of plants, some of which drips to the ground. Prior studies in Hawai'i indicate that CWI is highly variable and can contribute substantially to total precipitation. In this study, we monitored CWI and other processes at five mountain field sites on the Islands of Oʻahu, Maui, and Hawaiʻi to explore how CWI (1) varies with different climate and vegetation characteristics, (2) affects plant water use and growth, and (3) contributes to water resources.\nResults show that annual CWI varied from 158 to 910 mm, accounting for 3-34% of total water input at individual sites. This large variation was caused by differences in the quantity of cloud water, wind speed, and vegetation structure between sites. We developed a model to predict CWI using both climatic and forest canopy characteristics. On average, the model underestimated annual CWI by 18%, but reproduced the site differences relatively well. Plant water use decreased during periods of fog events mainly because of associated higher humidity. This new CWI model can be used to assess impacts of climate and land cover change on CWI and provide valuable information for resource management in Hawai‘i, which was not previously possible.\nAt one field site, we explored the impacts of fog water on hydrological and ecological processes. Fog effects on native plant growth were indirect, primarily buffering effects of solar radiation. Removal of grass allowed natural regeneration of seedlings but did not alter soil moisture values. A soil data-collection program was initiated to help evaluate the role CWI has in providing moisture for plants, reducing wildfire risk within the fog zone, and contributing to groundwater recharge to aquifers that supply drinking water and groundwater discharge to streams.","largerWorkTitle":"Pacific Island Climate Adaptation Science Center Final Technical Report","language":"English","publisher":"Climate Adaptation Science Centers","usgsCitation":"Tseng, H., Fortini, L., Mair, A., Kagawa-Viviani, A., Yelenik, S.G., Miyazawa, Y., Nullet, M.A., Kennedy, J., DeLay, J., Leopold, C., and Giambelluca, T., 2021, Cloud water interception in Hawai‘i: Developing capacity to characterize the spatial patterns and effects on water and ecological processes responses in Hawai‘i: Final Technical Report, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-132954","costCenters":[{"id":522,"text":"Pacific Islands Climate Science Center","active":true,"usgs":true},{"id":525,"text":"Pacific 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Alan 0000-0003-0302-6647 dmair@usgs.gov","orcid":"https://orcid.org/0000-0003-0302-6647","contributorId":4975,"corporation":false,"usgs":true,"family":"Mair","given":"Alan","email":"dmair@usgs.gov","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":true,"id":828777,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kagawa-Viviani, Aurora","contributorId":220317,"corporation":false,"usgs":false,"family":"Kagawa-Viviani","given":"Aurora","email":"","affiliations":[{"id":39036,"text":"University of Hawaii at Manoa","active":true,"usgs":false}],"preferred":false,"id":828778,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Yelenik, Stephanie G. 0000-0002-9011-0769","orcid":"https://orcid.org/0000-0002-9011-0769","contributorId":256836,"corporation":false,"usgs":false,"family":"Yelenik","given":"Stephanie","email":"","middleInitial":"G.","affiliations":[{"id":51875,"text":"formerly U.S. Geological Survey; currently Rocky Mountain Research Station, U.S. Forest Service","active":true,"usgs":false}],"preferred":false,"id":828779,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Miyazawa, Yoshiyuki","contributorId":214590,"corporation":false,"usgs":false,"family":"Miyazawa","given":"Yoshiyuki","email":"","affiliations":[{"id":39036,"text":"University of Hawaii at Manoa","active":true,"usgs":false}],"preferred":false,"id":828780,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nullet, Michael A","contributorId":214588,"corporation":false,"usgs":false,"family":"Nullet","given":"Michael","email":"","middleInitial":"A","affiliations":[{"id":39036,"text":"University of Hawaii at Manoa","active":true,"usgs":false}],"preferred":false,"id":828781,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kennedy, Joseph 0000-0002-6608-2366","orcid":"https://orcid.org/0000-0002-6608-2366","contributorId":203317,"corporation":false,"usgs":true,"family":"Kennedy","given":"Joseph","email":"","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":true,"id":828782,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"DeLay, John","contributorId":270226,"corporation":false,"usgs":false,"family":"DeLay","given":"John","affiliations":[{"id":56117,"text":"UH Honolulu Community College","active":true,"usgs":false}],"preferred":false,"id":828783,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Leopold, Christina 0000-0003-0499-3196","orcid":"https://orcid.org/0000-0003-0499-3196","contributorId":178961,"corporation":false,"usgs":false,"family":"Leopold","given":"Christina","affiliations":[],"preferred":false,"id":828784,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Giambelluca, Thomas 0000-0002-6798-3780","orcid":"https://orcid.org/0000-0002-6798-3780","contributorId":212176,"corporation":false,"usgs":false,"family":"Giambelluca","given":"Thomas","email":"","affiliations":[{"id":38449,"text":"University of Hawai‘i at Mānoa","active":true,"usgs":false}],"preferred":false,"id":828785,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70219047,"text":"70219047 - 2021 - Characterization of deep-sea coral and sponge communities in Greater Farallones National Marine Sanctuary: Point Arena South Essential Fish Habitat Conservation Area and New Amendment 28 Areas","interactions":[],"lastModifiedDate":"2021-03-22T14:00:40.139087","indexId":"70219047","displayToPublicDate":"2021-03-01T08:54:49","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":7778,"text":"National Marine Sactuaries Conservation Series","active":true,"publicationSubtype":{"id":1}},"title":"Characterization of deep-sea coral and sponge communities in Greater Farallones National Marine Sanctuary: Point Arena South Essential Fish Habitat Conservation Area and New Amendment 28 Areas","docAbstract":"This report summarizes samples collected during a remotely operated vehicle (ROV) cruise conducted in October 2019 on board E/V Nautilus. Areas sampled in Greater Farallones National Marine Sanctuary included areas proposed for fisheries management zoning in the Point Arena South (PAS) Essential Fish Habitat Conservation Area (EFH). Dive planning targeted habitats and biological communities of corals, sponges, and fishes in relation to the new, 2020 configuration of PAS EFH (hereafter referred to as PAS), which includes areas once closed to commercial bottom trawling and now opened to bottom trawling, once opened to bottom trawling and now closed, or that remain closed to commercial bottom trawling. Particular interest was given to enumerating deep-sea corals and sponges (DSCS) in these areas as they are long-lived, slow-growing species that are vulnerable to impacts from bottom trawling. Fish species were also enumerated. These data provide the most recent assessment and characterization for a portion of these areas before the final ruling on Amendment 28 went into effect on January 1, 2020 (50 C.F.R. part 660).
A total of seven sponge specimens were collected on this mission, some of which could potentially be new species, such as the large yellow ‘plate’-shaped sponge and the ‘palm frond’ morphology of the predatory sponge Asbestopluma, documented on both dives. Six coral collections were made, including three types of red Swiftia sp. gorgonians (two had fan-shaped morphology and one had branched morphology) with different polyp colors. A high diversity of fishes, particularly groundfish, were documented across the entire PAS area.
The findings from this cruise will be provided to NOAA’s National Marine Fisheries Service to help them identify biologically complex areas of the seafloor that are most sensitive to bottom trawling and aid in the ongoing management of this designated essential fish habitat conservation zone. Habitat data from these surveys will be used to confirm substrate prediction models that can be used to predict DSCS habitats where there is a dearth of visual observations.
","language":"English","publisher":"NOAA","usgsCitation":"Graiff, K., Roletto, J., Tezak, S., Williams, G.E., and Cochrane, G.R., 2021, Characterization of deep-sea coral and sponge communities in Greater Farallones National Marine Sanctuary: Point Arena South Essential Fish Habitat Conservation Area and New Amendment 28 Areas: National Marine Sactuaries Conservation Series, iv, 42 p.","productDescription":"iv, 42 p.","ipdsId":"IP-122453","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":384541,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Greater Farallones National Marine Sanctuary","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.34075927734375,\n 38.55997877925585\n ],\n [\n -123.67309570312499,\n 38.87392853923629\n ],\n [\n -123.75274658203126,\n 38.97008658346543\n ],\n [\n -124.00680541992188,\n 38.92522904714054\n ],\n [\n -123.71429443359375,\n 38.51271370850396\n ],\n [\n -123.34075927734375,\n 38.55997877925585\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Graiff, Kaitlin","contributorId":255549,"corporation":false,"usgs":false,"family":"Graiff","given":"Kaitlin","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":812559,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roletto, Jan","contributorId":152297,"corporation":false,"usgs":false,"family":"Roletto","given":"Jan","email":"","affiliations":[{"id":18902,"text":"Gulf of the Farallones National Marine Sanctuary","active":true,"usgs":false}],"preferred":false,"id":812560,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tezak, Sage","contributorId":255550,"corporation":false,"usgs":false,"family":"Tezak","given":"Sage","email":"","affiliations":[{"id":36803,"text":"NOAA","active":true,"usgs":false}],"preferred":false,"id":812561,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Williams, Gary E.","contributorId":198924,"corporation":false,"usgs":false,"family":"Williams","given":"Gary","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":812562,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cochrane, Guy R. 0000-0002-8094-4583 gcochrane@usgs.gov","orcid":"https://orcid.org/0000-0002-8094-4583","contributorId":2870,"corporation":false,"usgs":true,"family":"Cochrane","given":"Guy","email":"gcochrane@usgs.gov","middleInitial":"R.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":812563,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70218744,"text":"70218744 - 2021 - Avoidance of cold-, cool-, and warm-water fishes to Zequanox® exposure","interactions":[],"lastModifiedDate":"2021-06-01T17:47:28.644424","indexId":"70218744","displayToPublicDate":"2021-03-01T08:25:15","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2655,"text":"Management of Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Avoidance of cold-, cool-, and warm-water fishes to Zequanox® exposure","docAbstract":"Zequanox® is a biopesticide registered by the U.S. Environmental Protection Agency (USEPA) and the Canadian Pest Management Regulatory Agency for controlling dreissenid mussels with demonstrated selective toxicity. However, some research has indicated that Zequanox may impact the body condition and survival of some non-target species. We assessed avoidance behaviors of two species of cold-, cool-, and warm-water fishes to Zequanox at the maximum concentration allowed by the USEPA label (100 mg/L as active ingredient). Naïve, juvenile fish (n = 30 per species) were individually observed in a two-flume choice tank through which Zequanox-treated and untreated water simultaneously flowed in an unobstructed arena. Individual fish were observed during an untreated control period (20 min) and two Zequanox-exposure periods (20 min each). Treatment was alternated between arena sides to account for potential side bias in the test subjects. Positional data were collected and tabulated in real time with EthoVision® XT software. Zequanox concentrations and water quality properties (pH, dissolved oxygen, temperature, and specific conductance) were monitored during each trial. Analysis of treatment response was performed using a contrast within linear mixed-effects models. Our results indicate that Brook Trout, Lake Trout, and Bluegill avoided Zequanox-treated water, Yellow Perch were indifferent to Zequanox-treated water, and Lake Sturgeon and Fathead Minnow were attracted to Zequanox-treated water. These results combined with existing species sensitivity literature may help inform resource managers of potential treatment-related risks.
","language":"English","publisher":"Regional Euro-Asian Biological Invasions Centre - REABIC","doi":"10.3391/mbi.2021.12.1.07","usgsCitation":"Barbour, M., Luoma, J.A., Severson, T.J., Wise, J.K., and Bennie, B., 2021, Avoidance of cold-, cool-, and warm-water fishes to Zequanox® exposure: Management of Biological Invasions, v. 12, no. 1, p. 96-107, https://doi.org/10.3391/mbi.2021.12.1.07.","productDescription":"12 p.","startPage":"96","endPage":"107","ipdsId":"IP-111883","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":453273,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://doi.org/10.3391/mbi.2021.12.1.07","text":"Publisher Index Page"},{"id":436481,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BWGW8F","text":"USGS data release","linkHelpText":"Avoidance behavior of cold-, cool-, and warmwater fish exposed to Zequanox in a two-choice preference chamber, data release"},{"id":385080,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Barbour, Matthew T. 0000-0002-0095-9188 mbarbour@usgs.gov","orcid":"https://orcid.org/0000-0002-0095-9188","contributorId":195580,"corporation":false,"usgs":true,"family":"Barbour","given":"Matthew","email":"mbarbour@usgs.gov","middleInitial":"T.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":811580,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Luoma, James A. 0000-0003-3556-0190 jluoma@usgs.gov","orcid":"https://orcid.org/0000-0003-3556-0190","contributorId":4449,"corporation":false,"usgs":true,"family":"Luoma","given":"James","email":"jluoma@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":811581,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Severson, Todd J. 0000-0001-5282-3779 tseverson@usgs.gov","orcid":"https://orcid.org/0000-0001-5282-3779","contributorId":4749,"corporation":false,"usgs":true,"family":"Severson","given":"Todd","email":"tseverson@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":811582,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wise, Jeremy K. 0000-0003-0184-6959 jwise@usgs.gov","orcid":"https://orcid.org/0000-0003-0184-6959","contributorId":5009,"corporation":false,"usgs":true,"family":"Wise","given":"Jeremy","email":"jwise@usgs.gov","middleInitial":"K.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":811583,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bennie, Barbara","contributorId":257430,"corporation":false,"usgs":false,"family":"Bennie","given":"Barbara","email":"","affiliations":[],"preferred":false,"id":814234,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70218685,"text":"70218685 - 2021 - Comparing tree-ring based reconstructions of snowpack variability at different scales for the Navajo Nation","interactions":[],"lastModifiedDate":"2021-03-05T13:57:13.433738","indexId":"70218685","displayToPublicDate":"2021-03-01T07:53:55","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5567,"text":"Climate Services","active":true,"publicationSubtype":{"id":10}},"title":"Comparing tree-ring based reconstructions of snowpack variability at different scales for the Navajo Nation","docAbstract":"Snowpack in the western U.S. is on the decline, largely attributed to increasing temperatures in the region. This is a critical issue for many Native American communities who disproportionately rely on local snow-fed water supplies. In light of a combined ongoing drought and limited climate information for the Navajo Nation, Navajo water managers face decision-making challenges complicated by past and future climate uncertainty. Developed in partnership with the Navajo Nation Water Management Branch, this study documents two snowpack reconstruction options to address Navajo concerns about the amount and variability of snowpack in the Chuska Mountains. We used two separate snowpack datasets with tree rings collected in northern Arizona to develop and evaluate reconstructions of Chuska snowpack and their potential relevance and usefulness to Navajo water managers’ decision-making. We found that both reconstructions skillfully estimated snowpack, though there were differences that may have meaningful implications for water managers. Major snow droughts occurred roughly once per century over the last 300 years, with droughts in 1728–1744, 1818–1834, 1950–1977, and 1999–2006. Extremely dry individual years in each reconstruction punctuate multi-year drought periods in a way that has not been recognized from instrumental data alone and that can have a large influence on the overall intensity of a given drought. The reconstruction that is most representative of Chuska snowpack has less explanatory power than the regionally representative reconstruction, but the Chuska reconstruction effectively captures snowpack extremes and snow drought timing unique to the Chuska Mountains, and may hold greater relevance to Navajo water management.
Eruption of the Old Crow tephra deposited ~200 km3 of volcanic ash throughout Alaska and the northwestern Yukon (eastern Beringia), providing an isochronous marker across the region on a scale unique in the Pleistocene. The Old Crow tephra represents a critical temporal piercing point used extensively to link geographically disparate stratigraphic sections and the paleo-environmental records they contain. Although the canonical age of the Old Crow suggests eruption during the transition between the glacial and interglacial periods of Marine Isotope Stages (MIS) 5 and 6 at ~125 ka, recent U–Th–Pb and (U–Th)/He zircon dating of the tephra suggests eruption at ~200 ka, within MIS 7. If accurate, this revised eruption age begets significant change to existing models describing the geologic and biotic evolution of Beringia in the Pleistocene. Thus, confidently knowing the age of the tephra is critical to its time-stratigraphic utility and for past and future work in the region where the tephra has been found. With this contribution, we review existing Old Crow age constraints and present an eruption age for the tephra determined via high spatial resolution ion microprobe U–Pb surface analysis on zircon crystals isolated from source-proximal (<500 km from plausible source) pumiceous pyroclasts of the tephra. By dating only glass-mantled crystals isolated from discrete pumice clasts, we limit the potential for sample contamination from exotic crystals and resulting age bias. The young population of dates from this dataset corroborate previous radiometric dates and confirm Old Crow eruption within late MIS 7 at 207 ± 13 ka.
Despite abundant research documenting that land use/land cover (LULC) have substantial impacts on the hydrology of humid tropical systems, field-based evidence for the physical mechanisms behind these impacts are still lacking. In particular, our understanding of the hydrologic flowpaths that generate runoff in these systems, and how they vary with respect to LULC is insufficient to inform both physically-based hydrologic modeling and land-use decision-making. In this study, we use end-member mixing analysis (EMMA) of stream chemistry, and hydrometric characterizations of hillslope soil moisture to identify hydrologic flowpaths in humid tropical steep-land catchments of varying LULC: mature tropical forest, young secondary tropical forest, cattle pasture. EMMA was applied to data from 14 storm events (six at the mature forest, five at the young secondary forest, and three at the cattle pasture) that were intensively sampled during the 2017 wet season representing a wide range of rainfall magnitudes and intensities. Additionally, volumetric-soil-moisture responses at multiple depths were characterized during and after 74 storm events occurring from 2015 to 2017. EMMA results indicated that lateral preferential flow within the top 30 cm of the soil profile was a dominant source of runoff generation at the two forested catchments, with the contribution of this flow path increasing with rainfall magnitude and intensity. This was corroborated by volumetric-soil-moisture data, that showed that a perched zone of saturation developed at 30 cm at the time of peak storm runoff during the largest events and lasted for the remaining duration of the event. EMMA indicated that runoff was a combination of infiltration-excess overland flow and lateral subsurface flow in the actively grazed pastoral catchment. There, overland flow contributed 62 % of runoff during the highest runoff rate sampled (35.3 mm/hr) and this contribution increased substantially with storm magnitude. This flowpath identification was also supported by volumetric-soil-moisture data at the pasture, with peak saturation at all depths during the largest storm events occurring up to 30 min after peak runoff. These results provide a mechanistic explanation for previously observed hydrologic differences among tropical LULCs. Additionally, the wide range of hydrologic conditions during these storm events provide a basis for understanding how future changes to this, and similar humid tropical regions will impact hydrological processes and water availability.
Environmental DNA (eDNA) surveys are appealing options for monitoring aquatic biodiversity. While factors affecting eDNA persistence, capture and amplification have been heavily studied, watershed-scale surveys of fish communities and our confidence in such need further exploration.
We characterized fish eDNA compositions using rapid, low-volume filtering with replicate and control samples scaled for a single Illumina MiSeq flow cell, using the mitochondrial 12S ribosomal RNA locus for taxonomic profiling. Our goals were to determine: (1) spatiotemporal variation in eDNA abundance, (2) the filtrate needed to achieve strong sequencing libraries, (3) the taxonomic resolution of 12S ribosomal sequences in the study environment, (4) the portion of the expected fish community detectable by 12S sequencing, (5) biases in species recovery, (6) correlations between eDNA compositions and catch per unit effort (CPUE) and (7) the extent that eDNA profiles reflect major watershed features. Our bioinformatic approach included (1) estimation of sequencing error from unambiguous mappings and simulation of taxonomic assignment error under various mapping criteria; (2) binning of species based on inferred assignment error rather than by taxonomic rank; and (3) visualization of mismatch distributions to facilitate discovery of distinct haplotypes attributed to the same reference. Our approach was implemented within the St. Regis River, NY, USA, which supports tribal and recreational fisheries and has been a target of restoration activities. We used a large record of St. Regis-specific observations to validate our assignments.
We found that 300 mL drawn through 25-mm cellulose nitrate filters yielded greater than 5 ng/µL DNA at most sites in summer, which was an approximate threshold for generating strong sequencing libraries in our hands. Using inferred sequence error rates, we binned 12S references for 110 species on a state checklist into 85 single-species bins and seven multispecies bins. Of 48 bins observed by capture survey in the St. Regis, we detected eDNA consistent with 40, with an additional four detections flagged as potential contaminants. Sixteen unobserved species detected by eDNA ranged from plausible to implausible based on distributional data, whereas six observed species had no 12S reference sequence. Summed log-ratio compositions of eDNA-detected taxa correlated with log(CPUE) (Pearson’s R = 0.655, P < 0.001). Shifts in eDNA composition of several taxa and a genotypic shift in channel catfish (Ictalurus punctatus) coincided with the Hogansburg Dam, NY, USA. In summary, a simple filtering apparatus operated by field crews without prior expertise gave useful summaries of eDNA composition with minimal evidence of field contamination. 12S sequencing achieved useful taxonomic resolution despite the short marker length, and data exploration with standard bioinformatic tools clarified taxonomic uncertainty and sources of error.
","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.10539","usgsCitation":"Cornman, R.S., McKenna, J.E., and Fike, J., 2021, Composition and distribution of fish environmental DNA in an Adirondack watershed: PeerJ, v. 9, e10539, 34 p., https://doi.org/10.7717/peerj.10539.","productDescription":"e10539, 34 p.","ipdsId":"IP-121794","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":453296,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.10539","text":"Publisher Index Page"},{"id":436485,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EEOAZK","text":"USGS data release","linkHelpText":"Taxonomic composition of environmental DNA acquired by filtration from the St. Regis River, New York"},{"id":383691,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York","otherGeospatial":"St. Regis River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.135498046875,\n 44.23732831822538\n ],\n [\n -74.4873046875,\n 44.23732831822538\n ],\n [\n -74.4873046875,\n 44.98034238084973\n ],\n [\n -75.135498046875,\n 44.98034238084973\n ],\n [\n -75.135498046875,\n 44.23732831822538\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"9","noUsgsAuthors":false,"publicationDate":"2021-02-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Cornman, Robert S. 0000-0001-9511-2192 rcornman@usgs.gov","orcid":"https://orcid.org/0000-0001-9511-2192","contributorId":5356,"corporation":false,"usgs":true,"family":"Cornman","given":"Robert","email":"rcornman@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":811096,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McKenna, James E. Jr. 0000-0002-1428-7597 jemckenna@usgs.gov","orcid":"https://orcid.org/0000-0002-1428-7597","contributorId":195894,"corporation":false,"usgs":true,"family":"McKenna","given":"James","suffix":"Jr.","email":"jemckenna@usgs.gov","middleInitial":"E.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":811097,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fike, Jennifer A. 0000-0001-8797-7823","orcid":"https://orcid.org/0000-0001-8797-7823","contributorId":207268,"corporation":false,"usgs":true,"family":"Fike","given":"Jennifer A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":811098,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70234266,"text":"70234266 - 2021 - Subducting oceanic basement roughness impacts on upper plate tectonic structure and a backstop splay fault zone activated in the southern Kodiak aftershock region of the Mw 9.2, 1964 megathrust rupture, Alaska","interactions":[],"lastModifiedDate":"2022-08-05T13:32:23.783492","indexId":"70234266","displayToPublicDate":"2021-02-25T08:25:13","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"Subducting oceanic basement roughness impacts on upper plate tectonic structure and a backstop splay fault zone activated in the southern Kodiak aftershock region of the Mw 9.2, 1964 megathrust rupture, Alaska","docAbstract":"In 1964, the Alaska margin ruptured in a giant Mw 9.2 megathrust earthquake, the 2nd largest during worldwide instrumental recording. The coseismic slip and aftershock region offshore Kodiak Island was surveyed in 1977 – 1981 to understand the region’s tectonics. We re-processed multichannel seismic (MCS) field data using current standard Kirchhoff depth migration and/or MCS traveltime tomography. Further surveys in 1994 added P-wave velocity structure from wide-angle seismic lines and multibeam bathymetry. Published regional gravity, backscatter, and earthquake compilations also became available at this time.
Beneath the trench, rough oceanic crust is covered by ~3 to 5 km thick sediment. Sediment on the subducting plate modulates the plate interface relief. The accreted prism’s imbricate thrust faults have a complex P-wave velocity structure. Landward, an accelerated increase in P-wave velocities is marked by a backstop splay fault zone (BSFZ) that marks a transition from the prism to the higher rigidity rock beneath the middle and upper slope. Structures associated with this feature may indicate fluid flow. Further upslope, another fault extends >100 km along-strike across the middle slope. Erosion from subducting seamounts leaves embayments in the frontal prism.
Plate interface roughness varies along the subduction zone. Beneath the lower and middle slope, 2.5 D plate interface images show modest relief whereas the oceanic basement image is rougher. The 1964 earthquake slip maximum coincides with the leading/landward flank of a subducting seamount and the BSFZ. The BSFZ is a potentially active structure and should be considered in tsunami hazard assessments.
","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02275.1","usgsCitation":"Krabbenhoeft, A., von Huene, R.E., Miller, J.J., and Klaeschen, D., 2021, Subducting oceanic basement roughness impacts on upper plate tectonic structure and a backstop splay fault zone activated in the southern Kodiak aftershock region of the Mw 9.2, 1964 megathrust rupture, Alaska: Geosphere, v. 17, no. 2, p. 409-437, https://doi.org/10.1130/GES02275.1.","productDescription":"29 p.","startPage":"409","endPage":"437","ipdsId":"IP-118908","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":453307,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02275.1","text":"Publisher Index Page"},{"id":404873,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Kodiak Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -153.96240234375,\n 55.29788360510556\n ],\n [\n -150.46875,\n 55.29788360510556\n ],\n [\n -150.46875,\n 56.49889156789072\n ],\n [\n -153.96240234375,\n 56.49889156789072\n ],\n [\n -153.96240234375,\n 55.29788360510556\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"17","issue":"2","noUsgsAuthors":false,"publicationDate":"2021-02-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Krabbenhoeft, Anne","contributorId":208084,"corporation":false,"usgs":false,"family":"Krabbenhoeft","given":"Anne","email":"","affiliations":[{"id":37708,"text":"GEOMAR Helmholtz Center for Ocean Research Kiel, Wischhofstr. 1-3, 24148 Kiel, Germany","active":true,"usgs":false}],"preferred":false,"id":848365,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"von Huene, Roland E. 0000-0003-1301-3866 rvonhuene@usgs.gov","orcid":"https://orcid.org/0000-0003-1301-3866","contributorId":191070,"corporation":false,"usgs":true,"family":"von Huene","given":"Roland","email":"rvonhuene@usgs.gov","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":7065,"text":"USGS emeritus","active":true,"usgs":false}],"preferred":false,"id":848366,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, John J. 0000-0002-9098-0967 jmiller@usgs.gov","orcid":"https://orcid.org/0000-0002-9098-0967","contributorId":3785,"corporation":false,"usgs":true,"family":"Miller","given":"John","email":"jmiller@usgs.gov","middleInitial":"J.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":848367,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Klaeschen, Dirk","contributorId":198022,"corporation":false,"usgs":false,"family":"Klaeschen","given":"Dirk","email":"","affiliations":[],"preferred":false,"id":848368,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70223391,"text":"70223391 - 2021 - Accommodating the role of site memory in dynamic species distribution models","interactions":[],"lastModifiedDate":"2021-08-25T12:33:51.688114","indexId":"70223391","displayToPublicDate":"2021-02-25T07:30:31","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1465,"text":"Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Accommodating the role of site memory in dynamic species distribution models","docAbstract":"First-order dynamic occupancy models (FODOMs) are a class of state-space model in which the true state (occurrence) is observed imperfectly. An important assumption of FODOMs is that site dynamics only depend on the current state and that variations in dynamic processes are adequately captured with covariates or random effects. However, it is often difficult to understand and/or measure the covariates that generate ecological data, which are typically spatiotemporally correlated. Consequently, the non-independent error structure of correlated data causes underestimation of parameter uncertainty and poor ecological inference. Here, we extend the FODOM framework with a second-order Markov process to accommodate site memory when covariates are not available. Our modeling framework can be used to make reliable inference about site occupancy, colonization, extinction, turnover, and detection probabilities. We present a series of simulations to illustrate the data requirements and model performance. We then applied our modeling framework to 13 yr of data from an amphibian community in southern Arizona, USA. In this analysis, we found residual temporal autocorrelation of population processes for most species, even after accounting for long-term drought dynamics. Our approach represents a valuable advance in obtaining inference on population dynamics, especially as they relate to metapopulations.
Each mineral commodity chapter of the 2021 edition of the U.S. Geological Survey (USGS) Mineral Commodity Summaries (MCS) includes information on events, trends, and issues for each mineral commodity as well as discussions and tabular presentations on domestic industry structure, Government programs, tariffs, 5-year salient statistics, and world production and resources. The MCS is the earliest comprehensive source of 2020 mineral production data for the world. More than 90 individual minerals and materials are covered by 2-page synopses.
For mineral commodities for which there is a Government stockpile, detailed information concerning the stockpile status is included in the 2-page synopsis.
Abbreviations and units of measure and definitions of selected terms used in the report are in Appendix A and Appendix B, respectively. Reserves and resources information is in Appendix C, which includes “Part A—Resource and Reserve Classification for Minerals” and “Part B—Sources of Reserves Data.” A directory of USGS minerals information country specialists and their responsibilities is in Appendix D.
The USGS continually strives to improve the value of its publications to users. Constructive comments and suggestions by readers of the MCS 2021 are welcomed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/mcs2021","usgsCitation":"U.S. Geological Survey, 2021, Mineral commodity summaries 2021: U.S. Geological Survey, 200 p., https://doi.org/10.3133/mcs2021.","productDescription":"200 p.","numberOfPages":"200","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-125100","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":382519,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/periodicals/mcs2021/coverthb.jpg"},{"id":382587,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/periodicals/mcs2021/mcs2021.pdf","text":"Report","size":"11.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"MCS 2021"},{"id":382588,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://www.usgs.gov/centers/nmic/mineral-commodity-summaries","text":"Mineral Commodity Summaries Prior to 2021"},{"id":382589,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://www.usgs.gov/centers/nmic/commodity-statistics-and-information","text":"Commodity Statistics and Information"}],"contact":"Director, National Minerals Information Center
U.S. Geological Survey
12201 Sunrise Valley Drive
988 National Center
Reston, VA 20192
Email: nmicrecordsmgt@usgs.gov
Steady-state numerical groundwater-flow models were constructed for the islands of Kaua‘i, O‘ahu, and Maui to enable quantification of the hydrologic consequences of withdrawals and other stresses that can place limits on groundwater availability. The volcanic aquifers of Hawai‘i supply nearly all drinking water for the islands’ residents, freshwater for diverse industries, and natural discharge to springs, streams, and nearshore areas that support ecosystems, cultural practices, aesthetics, and recreation. Increases in groundwater withdrawal and changes in climate can cause water-table depression, saltwater rise, and reduction of natural groundwater discharge—all of which can limit fresh groundwater availability. The numerical models described in this report are designed to quantify these consequences. Separate models were created for each island using MODFLOW-2005 with the Seawater Intrusion package, which allows simulation of freshwater and saltwater in ocean-island aquifers. Calibration resulted in models that generally replicate observed water-level, stream base-flow, and spring-flow data, and simulate groundwater-flow directions and fresh groundwater thicknesses that are consistent with conceptual models. The calibrated models use hydraulic properties that are consistent with the ranges reported in previous studies. The models show that the relative distribution of fresh groundwater discharge to the ocean, streams, and springs and withdrawals for human use differ substantially among the three islands studied here. These differences indicate that consequences that limit the availability of fresh groundwater for human use are likely to differ among the three islands.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205126","usgsCitation":"Izuka, S.K., Rotzoll, K., and Nishikawa, T., 2021, Volcanic Aquifers of Hawai‘i—Construction and calibration of numerical models for assessing groundwater availability on Kaua‘i, O‘ahu, and Maui: U.S. Geological Survey Scientific Investigations Report 2020-5126, 63 p., https://doi.org/10.3133/sir20205126.","productDescription":"Report: viii, 63 p.; Data Release","numberOfPages":"63","ipdsId":"IP-071367","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":383611,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5126/covrthb.jpg"},{"id":383612,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5126/sir20205126.pdf","text":"Report","size":"53 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":383613,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K4DK2P","linkHelpText":"MODFLOW-2005 and SWI2 models for assessing groundwater availability in volcanic aquifers on Kaua‘i, O‘ahu, and Maui, Hawai‘i"},{"id":416444,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20155164","text":"Scientific Investigations Report 2015-5164","description":"Izuka, S.K., Engott, J.A., Rotzoll, Kolja, Bassiouni, Maoya, Johnson, A.G., Miller, L.D., and Mair, Alan, 2018, Volcanic aquifers of Hawai‘i—Hydrogeology, water budgets, and conceptual models (ver. 2.0, March 2018): U.S. Geological Survey Scientific Investigations Report 2015-5164, 158 p., https://doi.org/10.3133/sir20155164.","linkHelpText":"- Volcanic Aquifers of Hawai‘i—Hydrogeology, Water budgets, and Conceptual Models"},{"id":416445,"rank":5,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/pp1876","text":"Professional Paper 1876","description":"Izuka, S.K., and Rotzoll, K., 2023, Volcanic aquifers of Hawaiʻi—Contributions to assessing groundwater availability on Kauaʻi, Oʻahu, and Maui: U.S. Geological Survey Professional Paper 1876, 100 p., https://doi.org/10.3133/pp1876.","linkHelpText":"- Volcanic Aquifers of Hawai‘i—Contributions to Assessing Groundwater Availability on Kaua‘i, O‘ahu, and Maui"},{"id":417944,"rank":6,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20233010","text":"Fact Sheet 2023-3010","description":"Izuka, S.K., and Rotzoll, K., 2023, Availability of groundwater from the volcanic aquifers of the Hawaiian Islands: U.S. Geological Survey Fact Sheet 2023-3010, 4 p., https://doi.org/10.3133/fs20233010.","linkHelpText":"- Availability of Groundwater from the Volcanic Aquifers of the Hawaiian Islands"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kaua'i, Maui, O'ahu","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.73095703125,\n 20.57365332356332\n ],\n [\n -155.90423583984375,\n 20.57365332356332\n ],\n [\n -155.90423583984375,\n 21.04861794324536\n ],\n [\n -156.73095703125,\n 21.04861794324536\n ],\n [\n -156.73095703125,\n 20.57365332356332\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -158.33221435546875,\n 21.235622362422877\n ],\n [\n -157.62359619140625,\n 21.235622362422877\n ],\n [\n -157.62359619140625,\n 21.72505868324388\n ],\n [\n -158.33221435546875,\n 21.72505868324388\n ],\n [\n -158.33221435546875,\n 21.235622362422877\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -159.85931396484375,\n 21.830906665069758\n ],\n [\n -159.22622680664062,\n 21.830906665069758\n ],\n [\n -159.22622680664062,\n 22.264951388846296\n ],\n [\n -159.85931396484375,\n 22.264951388846296\n ],\n [\n -159.85931396484375,\n 21.830906665069758\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
Pacific Islands Water Science Center
U.S. Geological Survey
Inouye Regional Center
1845 Wasp Blvd., B176
Honolulu, HI 96818
Multispectral remote sensing data acquired by the Landsat 8 Operational Land Imager (OLI) sensor were analyzed using a new, automated technique to generate a map of exposed mineral and vegetation groups in the western San Juan Mountains, Colo., and the Four Corners Region of the United States. Band ratio results were combined into displayed mineral and vegetation groups using Boolean algebra. New analysis logic has been implemented to exploit the coastal aerosol band in Landsat 8 OLI data and identify concentrations of iron sulfate minerals. These may indicate the presence of near-surface pyrite, which can be a potential nonpoint source of acid rock drainage. Zoned occurrences of iron sulfate minerals in areas surrounding and down gradient of known sources of pyrite have been mapped using this technique. They show high correlation with occurrences of jarosite-bearing mineral assemblages, as mapped using airborne imaging spectrometer data and supporting field verification surveys. Mapping the occurrence of iron sulfate minerals produced by the weathering and oxidation of pyrite in exposed hydrothermally altered rocks can also provide an important indicator of the genetic environment of alteration and the associated mineral deposit type. The automated analysis methodology is being employed to rapidly and cost-effectively generate maps of large regions of the United States in support of U.S. Geological Survey mineral resource and mineral-environmental assessments. This map, which includes the ERDAS IMAGINE thematic raster format in the data release, has been attributed by pixel value with mineral and vegetation group classification data, which can be queried in most image processing and GIS software packages.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3466","usgsCitation":"Rockwell, B.W., Gnesda, W.R., and Hofstra, A.H., 2021, Improved automated identification and mapping of iron sulfate minerals, other mineral groups, and vegetation using Landsat 8 Operational Land Imager data, San Juan Mountains, Colorado, and Four Corners Region: U.S. Geological Survey Scientific Investigations Map 3466, 1 sheet, scale 1:325,000, 37-p. pamphlet, https://doi.org/10.3133/sim3466.","productDescription":"Pamphlet: v, 37 p.; 1 Sheet: 38.72 x 37.71 inches; 2 Data Releases: Read Me","onlineOnly":"Y","ipdsId":"IP-079681","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":382731,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ONOKKX","text":"USGS data release","linkHelpText":"Digital map of iron sulfate minerals, other mineral groups, and vegetation of the San Juan Mountains, Colorado, and Four Corners Region derived from automated analysis of Landsat 8 satellite data"},{"id":383537,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BYV5H4","text":"USGS data release","linkHelpText":"Digital map of iron sulfate minerals, other mineral groups, and vegetation of the western United States derived from automated analysis of Landsat 8 satellite data"},{"id":382727,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3466/coverthb2.jpg"},{"id":382729,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3466/sim3466_sheet1.pdf","text":"Sheet 1. Improved Automated Identification and Mapping of Iron Sulfate Minerals, Other Mineral Groups, and Vegetation using Landsat 8 Operational Land Imager Data, San Juan Mountains, Colorado, and Four Corners Region","size":"34.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3466 map sheet","linkHelpText":"Download file and view it in Adobe Acrobat DC or Adobe Reader DC to access interactive layers."},{"id":382728,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3466/sim3466_pamphlet.pdf","text":"Report","size":"2.99 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3466 pamphlet"},{"id":382730,"rank":4,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3466/ReadMe.txt","text":"Read Me","size":"12.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3466 map read me file"}],"country":"United States","state":"Colorado, Utah","otherGeospatial":"Four Corners region, San Juan Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.4737548828125,\n 36.70365959719456\n ],\n [\n -106.2872314453125,\n 38.50948995925553\n ],\n [\n -109.039306640625,\n 39.10022600175347\n ],\n [\n -110.31372070312499,\n 36.87522650673951\n ],\n [\n -107.4737548828125,\n 36.70365959719456\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Geology, Geophysics, and Geochemistry Science Center
U.S. Geological Survey
Box 25046, MS-973
Denver, CO 80225-0046
The ongoing global pandemic caused by coronavirus disease has once again demonstrated the role of the family Coronaviridae in causing human disease outbreaks. Because severe acute respiratory syndrome coronavirus 2 was first detected in December 2019, information on its tropism, host range, and clinical manifestations in animals is limited. Given the limited information, data from other coronaviruses might be useful for informing scientific inquiry, risk assessment, and decision-making. We reviewed endemic and emerging infections of alphacoronaviruses and betacoronaviruses in wildlife, livestock, and companion animals and provide information on the receptor use, known hosts, and clinical signs associated with each host for 15 coronaviruses detected in humans and animals. This information can be used to guide implementation of a One Health approach that involves human health, animal health, environmental, and other relevant partners in developing strategies for preparedness, response, and control to current and future coronavirus disease threats.
","language":"English","publisher":"Centers for Disease Control and Prevention","doi":"10.3201/eid2704.203945","usgsCitation":"Ghai, R.R., Carpenter, A., Liew, A.Y., Martin, K.B., Herring, M.K., Gerber, S.I., Hall, A.J., Sleeman, J.M., VonDobschuetz, S., and Barton Behravesh, C., 2021, Animal reservoirs and hosts for emerging alphacoronaviruses and betacoronaviruses: Emerging Infectious Diseases, v. 27, no. 4, p. 1015-1022, https://doi.org/10.3201/eid2704.203945.","productDescription":"8 p.","startPage":"1015","endPage":"1022","ipdsId":"IP-122283","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":453316,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3201/eid2704.203945","text":"Publisher Index Page"},{"id":383698,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"27","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ghai, Ria R.","contributorId":252886,"corporation":false,"usgs":false,"family":"Ghai","given":"Ria","email":"","middleInitial":"R.","affiliations":[{"id":50460,"text":"U.S. Centers for Disease Control and Prevention, Atlanta, GA, United States (","active":true,"usgs":false}],"preferred":false,"id":811067,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carpenter, Ann","contributorId":252887,"corporation":false,"usgs":false,"family":"Carpenter","given":"Ann","email":"","affiliations":[{"id":50461,"text":"U.S. Centers for Disease Control and Prevention, Atlanta, GA, United States","active":true,"usgs":false}],"preferred":false,"id":811068,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Liew, Amanda Y.","contributorId":252889,"corporation":false,"usgs":false,"family":"Liew","given":"Amanda","email":"","middleInitial":"Y.","affiliations":[{"id":50461,"text":"U.S. Centers for Disease Control and Prevention, Atlanta, GA, United States","active":true,"usgs":false}],"preferred":false,"id":811070,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Martin, Krystalyn B.","contributorId":252890,"corporation":false,"usgs":false,"family":"Martin","given":"Krystalyn","email":"","middleInitial":"B.","affiliations":[{"id":50461,"text":"U.S. Centers for Disease Control and Prevention, Atlanta, GA, United States","active":true,"usgs":false}],"preferred":false,"id":811071,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Herring, Meghan K.","contributorId":252888,"corporation":false,"usgs":false,"family":"Herring","given":"Meghan","email":"","middleInitial":"K.","affiliations":[{"id":50461,"text":"U.S. Centers for Disease Control and Prevention, Atlanta, GA, United States","active":true,"usgs":false}],"preferred":false,"id":811069,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gerber, Susan I.","contributorId":252891,"corporation":false,"usgs":false,"family":"Gerber","given":"Susan","email":"","middleInitial":"I.","affiliations":[{"id":50461,"text":"U.S. Centers for Disease Control and Prevention, Atlanta, GA, United States","active":true,"usgs":false}],"preferred":false,"id":811072,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hall, Aron J.","contributorId":252892,"corporation":false,"usgs":false,"family":"Hall","given":"Aron","email":"","middleInitial":"J.","affiliations":[{"id":50461,"text":"U.S. Centers for Disease Control and Prevention, Atlanta, GA, United States","active":true,"usgs":false}],"preferred":false,"id":811073,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Sleeman, Jonathan M. 0000-0002-9910-6125 jsleeman@usgs.gov","orcid":"https://orcid.org/0000-0002-9910-6125","contributorId":128,"corporation":false,"usgs":true,"family":"Sleeman","given":"Jonathan","email":"jsleeman@usgs.gov","middleInitial":"M.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":82110,"text":"Midcontinent Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":811074,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"VonDobschuetz, Sophie","contributorId":252893,"corporation":false,"usgs":false,"family":"VonDobschuetz","given":"Sophie","email":"","affiliations":[{"id":50462,"text":"Food and Agriculture Organization of the United Nations, Rome,","active":true,"usgs":false}],"preferred":false,"id":811075,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Barton Behravesh, Casey","contributorId":252894,"corporation":false,"usgs":false,"family":"Barton Behravesh","given":"Casey","email":"","affiliations":[{"id":50461,"text":"U.S. Centers for Disease Control and Prevention, Atlanta, GA, United States","active":true,"usgs":false}],"preferred":false,"id":811076,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ]}