The cratered highlands located northwest of Isidis Planitia have been recognized as one of the best preserved Noachian landscapes currently exposed on Mars; the area hosts a record of diverse surface processes, diagenesis, and aqueous alteration. This region has consistently been considered a high priority for landed-mission exploration and includes the anticipated landing site of the Mars 2020 Perseverance rover within Jezero crater. Past mapping, focused on Jezero crater and the surrounding area, Nili Planum, has varied in spatial extent, map scale, and purpose, though no previous maps have provided a continuous, high-resolution geologic map at uniform scale connecting the two locations. This map represents the first, large-scale, continuous geologic map spanning both Jezero crater and Nili Planum that is based on high-resolution images.
The map area contains the majority of both Jezero crater and Nili Planum at a publication map scale of 1:75,000, which was chosen to encompass the Jezero and southern Nili Planum landing sites under consideration for the Mars 2020 mission at the time of project initiation. This map covers an area that is exactly 1° by 1° (~60 by 60 km), spanning lat 76.8° N. to long 77.8° E. and lat 17.7° to long 18.7° N. The primary base map used for this geologic map is composed of Mars Reconnaissance Orbiter’s Context Camera (CTX) images, compiled into a 6 meter per pixel (m/pixel) mosaic. A nighttime Thermal Emission Imaging System 100 m/pixel image mosaic, digital terrain models constructed from CTX images, High-Resolution Stereo Camera (HRSC) topographic data, and High Resolution Imaging Science Experiment (HiRise) images also aided in unit identification and the assessment of stratigraphic relations. We defined map units on the basis of various characteristics visible in the CTX data at map scale, such as their texture, tone, morphology, marginal characteristics, geographic location, and stratigraphic relations to other units. Some units occur solely within Jezero crater, while Nili Planum contains a sequence of units that are present across the broader northwest Isidis Planitia region. Other units occur in both Jezero crater and Nili Planum, including bedrock, aeolian, and crater units. This map publication provides a regional geologic framework that connects the geologic units across Jezero crater and Nili Planum and the history they imply, facilitates future local-scale observations by landed missions of the Jezero crater and Nili Planum region, and enables the extrapolation of units that have been defined primarily by mineralogic composition to areas where there is no existing orbital spectroscopic data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3464","collaboration":"Prepared for the National Aeronautics and Space Administration","usgsCitation":"Sun, V.Z., and Stack, K.M., 2020, Geologic map of Jezero crater and the Nili Planum region, Mars: U.S. Geological Survey Scientific Investigations Map 3464, pamphlet 14 p., 1 sheet, scale 1:75,000, https://doi.org/10.3133/sim3464.","productDescription":"Pamphlet: iv, 14 p.; 1 Map: 56.60 x 45.62 inches; Metadata; Database; Read Me","numberOfPages":"14","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-118085","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":436704,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CZYIO7","text":"USGS data release","linkHelpText":"Interactive Map: USGS SIM 3464 Geologic Map of Jezero Crater and the Nili Planum Region"},{"id":380236,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3464/sim3464_pamphlet.pdf","text":"Pamphlet","size":"728 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3464 Pamphlet"},{"id":380235,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3464/sim3464.pdf","text":"Map","size":"36.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3464"},{"id":380240,"rank":7,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3464/sim3464_database.zip","size":"349.3 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3464 Database"},{"id":380239,"rank":6,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3464/sim3464_readme.txt","size":"4 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3464 Readme txt"},{"id":380238,"rank":5,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3464/sim3464_metadata.xml","size":"21 KB","linkFileType":{"id":8,"text":"xml"},"description":"SIM 3464 Metadata xml"},{"id":380237,"rank":4,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3464/sim3464_metadata.txt","size":"21 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3464 Metadata txt"},{"id":380234,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3464/coverthb.jpg"},{"id":400813,"rank":8,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://doi.org/10.5066/P9CZYIO7","text":"Interactive map","linkHelpText":"- Geologic Map of Jezero Crater and the Nili Planum Region, Mars, 1:75,000. Sun and Stack (2020)"}],"contact":"Contact Astrogeology Research Program staff
Astrogeology Science Center
U.S. Geological Survey
2255 N. Gemini Dr.
Flagstaff, AZ 86001
The purpose of this study is to characterize streamflow availability under natural (unregulated) low-flow conditions for streams in southeast Kaua‘i, Hawai‘i. The nine main study-area basins, from north to south, include Wailua River, Hanamā‘ulu, Nāwiliwili, Pūʻali, Hulēʻia, Waikomo, Lāwaʻi, and Wahiawa Streams, and Hanapēpē River. The results of this study can be used by water managers to develop technically sound instream-flow standards for the study-area streams.
Low-flow characteristics for natural streamflow conditions were represented by flow-duration discharges that are equaled or exceeded between 95 and 50 percent of the time. Short-term continuous-record stream-gaging stations that monitored low flows on Waiahi and right branch Lāwaʻi Streams were established to serve as potential index stations for partial-record sites in the study area. Continuous-record stream-gaging station on Hanapēpē River monitored natural flow during calendar year 2017 and the streamflow record during that period was used to estimate low-flow characteristics at the station. Partial-record sites were established on 3 main streams and 15 tributary streams, upstream from existing surface-water diversions. Low-flow characteristics were determined using historical and current streamflow data from continuous-record stream-gaging stations and miscellaneous sites, as well as additional data collected as part of this study. Low-flow-duration discharges for the following streams were estimated for the 59-year base period (water years 1961–2019) using two record-augmentation techniques: right branch ʻŌpaekaʻa Stream, North Fork Wailua River, north and south fork Waikoko Streams, ‘Ili‘ili‘ula Stream, north and south fork Hanamāʻulu Streams, Kamo‘oloa Stream, Pāohia Stream, Ku‘ia Stream, Lāwa‘i Stream, Wahiawa Stream, and Hanapēpē River. The 95-percent flow-duration discharges (Q95) ranged from 0.018 to 42 cubic feet per second (ft3/s). The 50-percent flow-duration discharges (Q50) ranged from 1.1 to 69 ft3/s. Upper-bound estimates of low-flow duration discharges at partial-record sites on south fork Hanamāʻulu, Hanamāʻulu tributary, ʻŌmaʻo, and Pōʻeleʻele Streams were estimated based on the highest discharges measured as part of this study during Q95 to Q50 flow conditions, which were 0.44, 0.40, 0.19, and 0.22 ft3/s, respectively. Measured discharges on Nāwiliwili, Pū‘ali, and left branch Wahiawa Streams do not correlate with data at any active long-term continuous-record stream-gaging stations (10 or more complete water years of natural-flow record) and therefore low-flow duration discharges could not be estimated.
This study also estimated streamflow gains and losses using seepage-run discharge measurements in eight of the nine study basins (Pūʻali Stream basin was excluded). A majority of the streams gained flow downstream from the uppermost diversions. Measured seepage-gain rates ranged between 0.03 and 24.3 ft3/s per mile of stream reach. Seepage gains are presumed to originate mainly from groundwater discharge in the Wailua River, Hanamā‘ulu Stream, Nāwiliwili Stream, Hulēʻia Stream, Lāwa‘i Stream, Wahiawa Stream, and Hanapēpē River basins. Under natural-flow conditions and flow conditions of the seepage runs, a majority of the study-area streams flow continuously from the mountains to the ocean. Where a stream discharges into a reservoir––Hanamā‘ulu and Wahiawa Streams––a dry reach may occur immediately downstream from the reservoir to the point of seepage gain in the stream.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205128","collaboration":"Prepared in cooperation with the State of Hawai‘i Commission on Water Resource Management","usgsCitation":"Cheng, C.L., 2020, Low-flow characteristics of streams from Wailua to Hanapēpē, Kauaʻi, Hawaiʻi: U.S. Geological Survey Scientific Investigations Report 2020–5128, 57 p., https://doi.org/10.3133/sir20205128.","productDescription":"viii, 57 p.","onlineOnly":"Y","ipdsId":"IP-119175","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":380936,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5128/sir20205128.pdf","text":"Report","size":"16.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5128"},{"id":380935,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5128/coverthb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Kaua‘i","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -159.35806274414062,\n 22.02072633149476\n ],\n [\n -159.43496704101562,\n 22.051277605102463\n ],\n [\n -159.554443359375,\n 22.071641456092383\n ],\n [\n -159.6258544921875,\n 22.03345683012737\n ],\n [\n -159.6533203125,\n 21.963424936844223\n ],\n [\n -159.65744018554688,\n 21.923937190109623\n ],\n [\n -159.59838867187497,\n 21.872969071537096\n ],\n [\n -159.43222045898438,\n 21.857675083878423\n ],\n [\n -159.33334350585935,\n 21.930306923001126\n ],\n [\n -159.31823730468747,\n 21.97106645968614\n ],\n [\n -159.33059692382812,\n 22.01945321869661\n ],\n [\n -159.35806274414062,\n 22.02072633149476\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Pacific Islands Water Science Center
Inouye Regional Center
1845 Wasp Blvd., B176
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Water samples from 122 sites in the U.S. Geological Survey National Water Quality Network were collected in 2013–17 to document ambient water-quality conditions in surface water of the United States and to determine status and trends of loads and concentrations for nutrients, contaminants, and sediment to estuaries and streams. Quality-control (QC) samples collected in the field with environmental samples were combined with QC samples from laboratory processing to provide information and documentation about the quality of the environmental data.
Quality assurance for inorganic and organic compounds assessed in the National Water Quality Network includes collection of field blanks to determine contamination bias and field replicates to determine variability bias. No contamination bias was found for 6 of the 13 nutrient compounds analyzed, and some potential contamination bias for some years was found for the other 7 nutrient compounds. Contamination bias was not found for carbon compounds or ultraviolet-absorbance measurements and was not assessed for sediment. All major ions and trace elements except potassium and lithium showed moderate contamination bias for at least 1 water year; generally, this bias was not at environmentally relevant concentrations. All compounds in the nutrient, carbon, and sediment group and in the major ions and trace elements group had low variability both in detection frequency and in concentration. Exceptions to this low variability were total particulate inorganic carbon and sediment for 2015, both of which are particulate substances with intrinsically high sampling variability.
The risk of contamination bias for pesticides in National Water Quality Network samples was low, as indicated by very few detections in field blanks. Sixteen pesticide compounds showed potential contamination bias based on unexpected detections in third-party blind spikes (false-positive results for compounds that are not included in the spike mixture of a sample, where the identity as a QC sample is unknown to the analyst), and 47 different compounds (out of 225 pesticide compounds) showed potential contamination bias from laboratory blanks. However, when timing and relative magnitudes of detections in blank samples, environmental samples, and benchmark concentrations are considered, most of this potential contamination is not relevant to interpretation of published pesticide results. Overall variability in detection frequency for pesticides from field replicates was low or moderate. Also based on field replicates, 55 pesticides had overall high variability in concentrations for at least 1 water year, although these assessments likely overestimate high variability.
At least 1 QC issue was found for 87 pesticides; however, most of the QC issues had no or little effect on the interpretation of environmental results because the U.S. Geological Survey National Water Quality Laboratory addressed the QC issue before publishing the environmental results, environmental results were almost entirely nondetections, concentrations of environmental results were higher than potential contamination bias, or benchmark concentrations were orders of magnitude higher than all environmental results. Eight compounds affected by two QC issues had a benchmark less than 100 nanograms per liter and warranted careful consideration of timing and magnitude of QC results in relation to surface-water results before interpretive use.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205116","usgsCitation":"Medalie, L., and Bexfield, L.M., 2020, Quality of data from the U.S. Geological Survey National Water Quality Network for water years 2013–17: U.S. Geological Survey Scientific Investigations Report 2020–5116, 21 p., https://doi.org/10.3133/sir20205116.","productDescription":"Report: v, 21 p.; Data Releases; 9 Tables","numberOfPages":"21","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-115536","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":436706,"rank":17,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94F31R8","text":"USGS data release","linkHelpText":"Nutrient and pesticide data collected from the USGS National Water Quality Network and previous networks, 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water years 2013–17"},{"id":380879,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5116/sir20205116_tables01-09.xlsx","text":"Tables 1-9","size":"221 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Tables 1-9 as a Microsoft Excel file"},{"id":380880,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5116/sir20205116_tables01-09_csv.zip","text":"CSV Package","size":"44 KB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Tables 1-9 as nine CSV files and a Read Me file"},{"id":380881,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5116/sir20205116_tables_readme.txt","text":"CSV Read Me","size":"19.6 KB","linkFileType":{"id":2,"text":"txt"},"linkHelpText":"- Read Me file to accompany the CSV files"},{"id":380887,"rank":13,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5116/sir20205116_table06.csv","text":"Table 6","size":"14.7 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U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
This report describes considerations for incorporating routine quality-assessment and quality-control evaluations into U.S. Geological Survey discrete water-sampling programs and projects. U.S. Geological Survey water-data science in 2020 is characterized by robustness, external reproducibility, collaborative large-volume data analysis, and efficient delivery of water-quality data. Confidence in data, or robustness, can be increased by supplementing traditional field-based quality-control data with laboratory quality control (QC) data, such as third-party blind spikes and blind blanks, laboratory blanks, and laboratory-reagent spikes. Laboratory quality-control data can provide additional information about bias and variability, method performance, and false-positive and false-negative rates that are not available from field QC data alone. Reproducibility is supported by means of standardizing metadata and documentation. Collaborative analysis brings together disparate elements of various types of quality-control review and communicates persistent data quality issues for compounds to data users internal and external to the U.S. Geological Survey. Efficient delivery of water-quality data is achieved when quality-control review is accomplished in the same expedited (near real-time) time frame as distribution of environmental results to the public and might be improved with consideration given to data versioning or to a system of alerting data users to data interpretation that might differ from originally published data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201109","usgsCitation":"Medalie, L., 2020, Considerations for incorporating quality control into water quality sampling strategies for the U.S. Geological Survey: U.S. Geological Survey Open-File Report 2020–1109, 5 p., https://doi.org/10.3133/ofr20201109.","productDescription":"iii, 5 p.","numberOfPages":"5","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-120022","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":380650,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1109/coverthb.jpg"},{"id":380651,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1109/ofr20201109.pdf","text":"Report","size":"935 KB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1109"}],"contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Assessment of chlorophyll-a, an algal pigment, typically measured by field and laboratory in situ analyses, is used to estimate algal abundance and trophic status in lakes and reservoirs. In situ-based monitoring programs can be expensive, may not be spatially, and temporally comprehensive and results may not be available in the timeframe needed to make some management decisions, but can be more accurate, precise, and specific than remotely sensed measures. Satellite remotely sensed chlorophyll-a offers the potential for more geographically and temporally dense data collection to support estimates when used to augment or substitute for in situ measures. In this study, we compare available chlorophyll-a data from in situ and satellite imagery measures at the national scale and perform a cost analysis of these different monitoring approaches. The annual potential avoided costs associated with increasing the availability of remotely sensed chlorophyll-a values were estimated to range between $5.7 and $316 million depending upon the satellite program used and the timeframe considered. We also compared sociodemographic characteristics of the regions (both public and private lands) covered by both remote sensing and in situ data to check for any systematic differences across areas that have monitoring data. This analysis underscores the importance of continued support for both field-based in situ monitoring and satellite sensor programs that provide complementary information to water quality managers, given increased challenges associated with eutrophication, nuisance, and harmful algal bloom events.
No abstract available.
","language":"English","publisher":"U.S. Geological Survey","usgsCitation":"McNiff, M., 2020, John Wesley Powell Center for Analysis and Synthesis Newsletter, Volume 6, Issue 1, v. 6, no. 1, 2 p.","productDescription":"2 p.","numberOfPages":"2","ipdsId":"IP-125059","costCenters":[{"id":29789,"text":"John Wesley Powell Center for Analysis and Synthesis","active":true,"usgs":true},{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"links":[{"id":383260,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":383603,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://www.usgs.gov/media/files/powell-center-newsletter-v-6-no-1","linkFileType":{"id":5,"text":"html"}}],"volume":"6","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McNiff, Marcia 0000-0003-0709-6992 mmcniff@usgs.gov","orcid":"https://orcid.org/0000-0003-0709-6992","contributorId":4025,"corporation":false,"usgs":true,"family":"McNiff","given":"Marcia","email":"mmcniff@usgs.gov","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":810197,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70218233,"text":"70218233 - 2020 - Infectious diseases in Yellowstone’s wolves","interactions":[],"lastModifiedDate":"2021-02-19T20:45:51.639295","indexId":"70218233","displayToPublicDate":"2020-12-01T14:42:56","publicationYear":"2020","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"9","title":"Infectious diseases in Yellowstone’s wolves","docAbstract":"No abstract available.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Yellowstone wolves: Science and discovery in the world's first national park","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"University of Chicago","usgsCitation":"Brandell, E.E., Almberg, E.S., Cross, P., Dobson, A.P., Smith, D., and Hudson, P.J., 2020, Infectious diseases in Yellowstone’s wolves, chap. 9 of Yellowstone wolves: Science and discovery in the world's first national park.","ipdsId":"IP-093319","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":383400,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Yellowstone National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.104736328125,\n 43.65197548731187\n ],\n [\n -109.2041015625,\n 43.65197548731187\n ],\n [\n -109.2041015625,\n 44.98811302615805\n ],\n [\n -111.104736328125,\n 44.98811302615805\n ],\n [\n -111.104736328125,\n 43.65197548731187\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Brandell, Ellen E. 0000-0002-2698-7013","orcid":"https://orcid.org/0000-0002-2698-7013","contributorId":207016,"corporation":false,"usgs":false,"family":"Brandell","given":"Ellen","email":"","middleInitial":"E.","affiliations":[{"id":25381,"text":"Penn State Univ.","active":true,"usgs":false}],"preferred":false,"id":810549,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Almberg, Emily S.","contributorId":198304,"corporation":false,"usgs":false,"family":"Almberg","given":"Emily","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":810550,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cross, Paul C. 0000-0001-8045-5213","orcid":"https://orcid.org/0000-0001-8045-5213","contributorId":204814,"corporation":false,"usgs":true,"family":"Cross","given":"Paul C.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":810551,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dobson, Andrew P","contributorId":251766,"corporation":false,"usgs":false,"family":"Dobson","given":"Andrew","email":"","middleInitial":"P","affiliations":[{"id":6644,"text":"Princeton University","active":true,"usgs":false}],"preferred":false,"id":810552,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Smith, Douglas W.","contributorId":179181,"corporation":false,"usgs":false,"family":"Smith","given":"Douglas W.","affiliations":[],"preferred":false,"id":810554,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hudson, Peter J.","contributorId":192149,"corporation":false,"usgs":false,"family":"Hudson","given":"Peter","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":810553,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70228595,"text":"70228595 - 2020 - Beyond neonicotinoids – Wild pollinators are exposed to a range of pesticides while foraging in agroecosystems","interactions":[],"lastModifiedDate":"2022-02-15T12:14:59.08284","indexId":"70228595","displayToPublicDate":"2020-12-01T13:19:57","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3352,"text":"Science of the Total Environment","active":true,"publicationSubtype":{"id":10}},"title":"Beyond neonicotinoids – Wild pollinators are exposed to a range of pesticides while foraging in agroecosystems","docAbstract":"Pesticide exposure is a growing global concern for pollinator conservation. While most current pesticide studies have specifically focused on the impacts of neonicotinoid insecticides toward honeybees and some native bee species, wild pollinators may be exposed to a broader range of agrochemicals. In 2016 and 2017 we collected a total of 637 wild bees and butterflies from the margins of cultivated agricultural fields situated on five Conservation Areas in mid-northern Missouri. Pollinators were composited by individual genera (90 samples) and whole tissues were then analyzed for the presence of 168 pesticides and degradation products. At least one pesticide was detected (% frequency) in the following wild bee genera: Bombus (96%), Eucera (75%), Melissodes (73%), Ptilothrix (50%), Xylocopa (50%), and Megachile (17%). Similarly, at least one pesticide was detected in the following lepidopteran genera: Hemaris (100%), Hylephila (75%), Danaus (60%), and Colias (50%). Active ingredients detected in >2% of overall pollinator samples were as follows: metolachlor (24%), tebuconazole (22%), atrazine (18%), imidacloprid desnitro (13%), bifenthrin (9%), flumetralin (9%), p, p'-DDD (6%), tebupirimfos (4%), fludioxonil (4%), flutriafol (3%), cyproconazole (2%), and oxadiazon (2%). Concentrations of individual pesticides ranged from 2 to 174 ng/g. Results of this pilot field study indicate that wild pollinators are exposed to and are potentially bioaccumulating a wide variety of pesticides in addition to neonicotinoids. Here, we provide evidence that wild bee and butterfly genera may face exposure to a wide range of insecticides, fungicides, and herbicides despite being collected from areas managed for conservation. Therefore, even with the presence of extensive habitat, minimal agricultural activity on Conservation Areas may expose pollinators to a range of pesticides.
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The neonicotinoid imidacloprid is commonly applied to hemlock (Tsuga spp.) stands in eastern North America to reduce tree mortality from infestations of the invasive hemlock woolly adelgid (HWA; Adelges tsugae). While laboratory and mesocosm studies have determined that imidacloprid can bioaccumulate in anurans and cause sublethal effects, no field studies have investigated whether salamanders or insects in streams adjacent to HWA treatments bioaccumulate imidacloprid or if sublethal effects are detectable in wild salamanders. We assessed relationships between imidacloprid exposure and stream salamander health in West Virginia, USA, using concentration of the stress hormone corticosterone and body condition indices (BCI) as response variables. Of 107 Desmognathus salamanders from 11 sites tested for bioaccumulation, we detected imidacloprid in 47 salamanders. Of 15 benthic macroinvertebrate samples tested, we detected imidacloprid, imidacloprid-urea, and imidacloprid-olefin in 15, 13, and 1 sample, respectively. Based on 115 Desmognathus salamanders sampled at 11 sites for stress hormone responses, corticosterone concentration increased with imidacloprid concentration in stream water. For 802 salamanders sampled at 48 sites, BCI decreased as concentration of imidacloprid in stream water increased, but explanatory power was low. Our study suggests that chronic leaching of imidacloprid from treated hemlock stands into adjacent streams has the potential to negatively affect aquatic organisms and may provide a route of exposure to higher trophic levels.
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L.","contributorId":264879,"corporation":false,"usgs":false,"family":"Finke","given":"D.","email":"","middleInitial":"L.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":823998,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70251804,"text":"70251804 - 2020 - Magmatic-tectonic settings of Cenozoic epithermal gold-silver deposits of the Great Basin, western United States","interactions":[],"lastModifiedDate":"2026-03-26T14:22:17.959724","indexId":"70251804","displayToPublicDate":"2020-12-01T10:54:41","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Magmatic-tectonic settings of Cenozoic epithermal gold-silver deposits of the Great Basin, western United States","docAbstract":"Numerous epithermal gold-silver deposits formed during the past 40 Ma are irregularly distributed across the Great Basin. These deposits formed in six major magmatic-tectonic settings that varied during the complex evolution of the continental margin of western North America: (1) slab rollback–ignimbrite flareup (~45–17 Ma), (2) slab rollback–ancestral Cascade arc (~35 Ma–present), (3) Yellowstone hotspot–bimodal (~16.7–3 Ma), (4) slab window (~16 Ma–present), (5) Basin and Range bimodal extensional (≤ 7 Ma), and (6) amagmatic extensional (≤ 5 Ma). Most large (> 1 Moz gold produced) deposits are Miocene (~20–8 Ma), low-, intermediate-, and high-sulfidation deposits in the southern part of the ancestral Cascade arc; late Miocene post-subduction, low-sulfidation deposits formed over the slab window; and lowsulfidation deposits related to early (16.7–15 Ma) Yellowstone hotspot magmatism formed along the northern Nevada rift and related fracture zones to the west. The world-class Round Mountain low-sulfidation deposit is the only large deposit in ignimbrite flareup rocks despite these rocks constituting the largest eruptive volume of Cenozoic magmas in the Great Basin. Intermediate to silicic composition lava dome complexes are the most common setting for epithermal deposits in the western Great Basin, whereas deposits formed in a wide range of settings and rock types during Yellowstone hotspot activity. With exception of the Round Mountain caldera, the dozens of calderas of the ignimbrite flareup do not host large epithermal deposits. Several young (≤ 5 Ma), “amagmatic” low-sulfidation deposits formed along Basin and Range fault zones in sedimentary rocks that lack proximal magmatic activity. The types and characteristics of epithermal gold-silver deposits in the Great Basin systematically vary with magmatic-tectonic setting and magma composition, and their distribution reflects the combined effects of tectonic setting of magma genesis; magma source, composition and eruptive style; crustal thickness and composition; presence of crustal-scale structural zones; climate; and preservation of deposits.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Vision for discovery, Geological Society of Nevada 2020 symposium proceedings","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Geological Society of Nevada","usgsCitation":"John, D.A., and Henry, C.S., 2020, Magmatic-tectonic settings of Cenozoic epithermal gold-silver deposits of the Great Basin, western United States, in Vision for discovery, Geological Society of Nevada 2020 symposium proceedings, p. 765-796.","productDescription":"32 p.","startPage":"765","endPage":"796","ipdsId":"IP-114651","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":501502,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Great Basin","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"John, David A. 0000-0001-7977-9106 djohn@usgs.gov","orcid":"https://orcid.org/0000-0001-7977-9106","contributorId":1748,"corporation":false,"usgs":true,"family":"John","given":"David","email":"djohn@usgs.gov","middleInitial":"A.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":895628,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Henry, Christopher S.","contributorId":42522,"corporation":false,"usgs":true,"family":"Henry","given":"Christopher","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":895629,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70249398,"text":"70249398 - 2020 - Coleonyx variegatus (Western Banded Gecko). Sterility","interactions":[],"lastModifiedDate":"2024-01-12T16:37:34.55497","indexId":"70249398","displayToPublicDate":"2020-12-01T10:23:59","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1898,"text":"Herpetological Review","active":true,"publicationSubtype":{"id":10}},"title":"Coleonyx variegatus (Western Banded Gecko). Sterility","docAbstract":"No abstract available.
","language":"English","publisher":"Society for the Study of Amphibians and Reptiles","usgsCitation":"Medica, P.A., 2020, Coleonyx variegatus (Western Banded Gecko). Sterility: Herpetological Review, v. 51, no. 4, p. 847-848.","productDescription":"2 p.","startPage":"847","endPage":"848","ipdsId":"IP-113543","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":424381,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Nevada","otherGeospatial":"Rock Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -116.33559790478063,\n 36.716299539685096\n ],\n [\n -116.33559790478063,\n 36.60454041491195\n ],\n [\n -116.19712268597902,\n 36.60454041491195\n ],\n [\n -116.19712268597902,\n 36.716299539685096\n ],\n [\n -116.33559790478063,\n 36.716299539685096\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"51","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Medica, Phil A. 0000-0002-5901-8841 pmedica@usgs.gov","orcid":"https://orcid.org/0000-0002-5901-8841","contributorId":3226,"corporation":false,"usgs":true,"family":"Medica","given":"Phil","email":"pmedica@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":885468,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70222377,"text":"70222377 - 2020 - Metallogenic implications of a new geodynamic model for the Eglab, Algeria","interactions":[],"lastModifiedDate":"2025-06-17T15:28:32.233318","indexId":"70222377","displayToPublicDate":"2020-12-01T10:20:57","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Metallogenic implications of a new geodynamic model for the Eglab, Algeria","docAbstract":"No abstract available.
","conferenceTitle":"5th Colloquium of the International Geoscience Programme (IGCP-638)","conferenceDate":"December 4-5, 2022","conferenceLocation":"Accra, Ghana","language":"English","publisher":"UNESCO (ICGP)","usgsCitation":"Taylor, C.D., Bradley, D., Finn, C.A., Zerrouki, A., Ayad, B., Belanteur, N.F., Bouchilaoune, N., Johnson, M., Meziane, G., Mihalasky, M.J., Mouchene, H., Oughou, S., Smith, S.M., Solano, F., and Zerrouk, S., 2020, Metallogenic implications of a new geodynamic model for the Eglab, Algeria, 5th Colloquium of the International Geoscience Programme (IGCP-638), Accra, Ghana, December 4-5, 2022, 3 p.","productDescription":"3 p.","ipdsId":"IP-111457","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":490840,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Taylor, Cliff D. 0000-0001-6376-6298 ctaylor@usgs.gov","orcid":"https://orcid.org/0000-0001-6376-6298","contributorId":1283,"corporation":false,"usgs":true,"family":"Taylor","given":"Cliff","email":"ctaylor@usgs.gov","middleInitial":"D.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":819862,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bradley, Dwight 0000-0001-9116-5289 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0000-0002-4149-3812","orcid":"https://orcid.org/0000-0002-4149-3812","contributorId":220949,"corporation":false,"usgs":false,"family":"Belanteur","given":"Nadjib","email":"","middleInitial":"F.","affiliations":[{"id":40298,"text":"Algerian Geological Survey Agency","active":true,"usgs":false}],"preferred":false,"id":819867,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bouchilaoune, Nabyl 0000-0002-2665-9845","orcid":"https://orcid.org/0000-0002-2665-9845","contributorId":261346,"corporation":false,"usgs":false,"family":"Bouchilaoune","given":"Nabyl","affiliations":[{"id":40298,"text":"Algerian Geological Survey Agency","active":true,"usgs":false}],"preferred":false,"id":819868,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Johnson, Michaela 0000-0001-6133-0247 mrjohns@usgs.gov","orcid":"https://orcid.org/0000-0001-6133-0247","contributorId":182462,"corporation":false,"usgs":true,"family":"Johnson","given":"Michaela","email":"mrjohns@usgs.gov","affiliations":[],"preferred":true,"id":819869,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Meziane, Ghania","contributorId":261347,"corporation":false,"usgs":false,"family":"Meziane","given":"Ghania","affiliations":[{"id":40298,"text":"Algerian Geological Survey Agency","active":true,"usgs":false}],"preferred":false,"id":819870,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Mihalasky, Mark J. 0000-0002-0082-3029 mjm@usgs.gov","orcid":"https://orcid.org/0000-0002-0082-3029","contributorId":3692,"corporation":false,"usgs":true,"family":"Mihalasky","given":"Mark","email":"mjm@usgs.gov","middleInitial":"J.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true},{"id":662,"text":"Western Mineral and Environmental Resources Science 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smsmith@usgs.gov","orcid":"https://orcid.org/0000-0003-3591-5377","contributorId":1460,"corporation":false,"usgs":true,"family":"Smith","given":"Steven","email":"smsmith@usgs.gov","middleInitial":"M.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":819874,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Solano, Federico 0000-0002-0308-5850 fsolanoc@usgs.gov","orcid":"https://orcid.org/0000-0002-0308-5850","contributorId":4302,"corporation":false,"usgs":true,"family":"Solano","given":"Federico","email":"fsolanoc@usgs.gov","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":819875,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Zerrouk, 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stratigraphic code to formalize subseries and subepochs","docAbstract":"At the 75th Annual Meeting of the North American Commission on Stratigraphic Nomenclature, 22 October, 2020, in connection with GSA 2020 Connects Online, the Commission voted unanimously to accept the revision of Articles 73, 81 and 82 of the North American Stratigraphic Code (North American Commission on Stratigraphic Nomenclature, 2005 with subsequent updates), and concomitant changes to Table 2; specific revisions of the Code are indicated in red color. These replace all older versions of the specified Articles. An application for this revision (Aubry et al. 2019) was published in Stratigraphy more than one year prior to the meeting; thus, the vote on this application for revision follows Article 21 of the Code.
","language":"English","publisher":"Micropaleontology Press","doi":"10.29041/strat.17.4.315-316","usgsCitation":"Aubry, M., Fluegeman, R.H., Edwards, L.E., Pratt, B.R., and Brett, C.E., 2020, North American commission on stratigraphic nomenclature report 14 – Revision of articles 25-27 of the North American stratigraphic code to formalize subseries and subepochs: Stratigraphy, v. 17, no. 4, p. 315-316, https://doi.org/10.29041/strat.17.4.315-316.","productDescription":"2 p.","startPage":"315","endPage":"316","ipdsId":"IP-123969","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":383167,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-12-11","publicationStatus":"PW","contributors":{"authors":[{"text":"Aubry, Marie-Pierre","contributorId":174332,"corporation":false,"usgs":false,"family":"Aubry","given":"Marie-Pierre","email":"","affiliations":[{"id":27421,"text":"Department of Earth and Planetary Sciences Rutgers University 610 Taylor Road Piscataway NJ 08854-8066, USA","active":true,"usgs":false}],"preferred":false,"id":806952,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fluegeman, Richard H.","contributorId":139942,"corporation":false,"usgs":false,"family":"Fluegeman","given":"Richard","email":"","middleInitial":"H.","affiliations":[{"id":13322,"text":"Ball State University","active":true,"usgs":false}],"preferred":false,"id":806953,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Edwards, Lucy E. 0000-0003-4075-3317 leedward@usgs.gov","orcid":"https://orcid.org/0000-0003-4075-3317","contributorId":2647,"corporation":false,"usgs":true,"family":"Edwards","given":"Lucy","email":"leedward@usgs.gov","middleInitial":"E.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":806954,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pratt, Brian R.","contributorId":214140,"corporation":false,"usgs":false,"family":"Pratt","given":"Brian","email":"","middleInitial":"R.","affiliations":[{"id":13248,"text":"University of Saskatchewan","active":true,"usgs":false}],"preferred":false,"id":806955,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brett, Carlton E.","contributorId":214141,"corporation":false,"usgs":false,"family":"Brett","given":"Carlton","email":"","middleInitial":"E.","affiliations":[{"id":7159,"text":"University of Cincinnati","active":true,"usgs":false}],"preferred":false,"id":806956,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70216755,"text":"70216755 - 2020 - An evaluation of noninvasive sampling techniques for Malayan sun bears","interactions":[],"lastModifiedDate":"2020-12-04T16:12:36.626286","indexId":"70216755","displayToPublicDate":"2020-12-01T10:03:07","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3671,"text":"Ursus","active":true,"publicationSubtype":{"id":10}},"title":"An evaluation of noninvasive sampling techniques for Malayan sun bears","docAbstract":"Traditional mark–recapture studies to estimate abundance and trends of Malayan sun bear (Helarctos malayanus) populations are impeded by logistics of live-trapping wild individuals. The development of noninvasive sampling techniques for monitoring sun bear populations is therefore crucial for targeted conservation action. Sun bears have short fur, and conventional hair-snagging devices are ineffective. Moreover, scats are rapidly decomposed by the warm, humid environment, as well as by invertebrates. In combination with camera-sampling, we tested 2 designs of hair traps (n = 45) in situ at Tabin Wildlife Reserve in Sabah, Malaysia, during April–October 2017, to obtain hair samples from wild sun bears. We also deployed 4 types of hair traps in rainforest enclosures with captive sun bears to evaluate hair-capture success and the effects of weathering, lure, and adhesive on polymerase chain reaction (PCR) amplification success. Wild adult male sun bears displayed back-rubbing behavior at hair traps and 6 individuals were identified based on unique chest marks. We collected 30 hair samples from wild sun bears, including 15 chest mark images of 6 individuals over 1,260 trap-nights. We detected adult males at hair traps more frequently than females and subadults. We obtained 39 hair samples in the captive trials. Extracted DNA from hair roots successfully amplified with mitochondrial (wild bears: 95%; captive bears: 97%) and microsatellite primers (wild bears: 100%; captive bears 87%). Adhesive and lure type did not affect PCR amplification, but weathering reduced amplification of microsatellite loci. This study is the first successful attempt to obtain genetic samples from wild sun bears using inexpensive, readily available materials such as duct tape, polybutyl glue, and locally sourced lures. The quality of genetic material from these genetic samples should be suitable for studies of population size and gene flow.
","language":"English","publisher":"BioOne","doi":"10.2192/URSUS-S-20-00004.1","usgsCitation":"Tee, T.L., Lai, W.L., Ju Wei, T.K., Shern, O.Z., van Manen, F.T., Sharp, S.P., Wong, S.T., Chew, J., and Ratnayeke, S., 2020, An evaluation of noninvasive sampling techniques for Malayan sun bears: Ursus, v. 31, e16, 12 p., https://doi.org/10.2192/URSUS-S-20-00004.1.","productDescription":"e16, 12 p.","ipdsId":"IP-116549","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":454708,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.2192/ursus-s-20-00004.1","text":"Publisher Index Page"},{"id":380986,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Malaysia","state":"Sabah","otherGeospatial":"Tabin Wildlife Reserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 117.44934082031249,\n 4.99792208960986\n ],\n [\n 118.77868652343751,\n 4.99792208960986\n ],\n [\n 118.77868652343751,\n 5.719845659536203\n ],\n [\n 117.44934082031249,\n 5.719845659536203\n ],\n [\n 117.44934082031249,\n 4.99792208960986\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"31","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tee, Thye Lim","contributorId":245374,"corporation":false,"usgs":false,"family":"Tee","given":"Thye","email":"","middleInitial":"Lim","affiliations":[{"id":49172,"text":"Sunway University","active":true,"usgs":false}],"preferred":false,"id":806080,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lai, Wai Ling","contributorId":245375,"corporation":false,"usgs":false,"family":"Lai","given":"Wai","email":"","middleInitial":"Ling","affiliations":[{"id":49172,"text":"Sunway University","active":true,"usgs":false}],"preferred":false,"id":806081,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ju Wei, Terence Kok","contributorId":245376,"corporation":false,"usgs":false,"family":"Ju Wei","given":"Terence","email":"","middleInitial":"Kok","affiliations":[{"id":49172,"text":"Sunway University","active":true,"usgs":false}],"preferred":false,"id":806082,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shern, Ooi Zhuan","contributorId":245377,"corporation":false,"usgs":false,"family":"Shern","given":"Ooi","email":"","middleInitial":"Zhuan","affiliations":[{"id":49172,"text":"Sunway University","active":true,"usgs":false}],"preferred":false,"id":806083,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"van Manen, Frank T. 0000-0001-5340-8489 fvanmanen@usgs.gov","orcid":"https://orcid.org/0000-0001-5340-8489","contributorId":2267,"corporation":false,"usgs":true,"family":"van Manen","given":"Frank","email":"fvanmanen@usgs.gov","middleInitial":"T.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":806084,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sharp, Stuart P.","contributorId":203981,"corporation":false,"usgs":false,"family":"Sharp","given":"Stuart","email":"","middleInitial":"P.","affiliations":[{"id":36781,"text":"Lancaster Environment Centre, Lancaster University, Lancaster, LA1 4YQ, UK","active":true,"usgs":false}],"preferred":false,"id":806085,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wong, Siew Te","contributorId":245378,"corporation":false,"usgs":false,"family":"Wong","given":"Siew","email":"","middleInitial":"Te","affiliations":[{"id":49173,"text":"Bornean Sun Bear Conservation Centre","active":true,"usgs":false}],"preferred":false,"id":806086,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Chew, Jactty","contributorId":245379,"corporation":false,"usgs":false,"family":"Chew","given":"Jactty","email":"","affiliations":[{"id":49172,"text":"Sunway University","active":true,"usgs":false}],"preferred":false,"id":806087,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ratnayeke, Shyamala","contributorId":203978,"corporation":false,"usgs":false,"family":"Ratnayeke","given":"Shyamala","email":"","affiliations":[{"id":36779,"text":"Department of Biological Sciences, Sunway University, Malaysia","active":true,"usgs":false}],"preferred":false,"id":806088,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70228379,"text":"70228379 - 2020 - A test of the Niche Variation Hypothesis in a ruminant herbivore","interactions":[],"lastModifiedDate":"2022-02-09T16:04:11.307727","indexId":"70228379","displayToPublicDate":"2020-12-01T09:55:30","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2158,"text":"Journal of Animal Ecology","active":true,"publicationSubtype":{"id":10}},"title":"A test of the Niche Variation Hypothesis in a ruminant herbivore","docAbstract":"Ecological risk assessments play an important role in environmental management and decision-making. Although empirical measurements of the effects of habitat changes and chemical exposure are often made at molecular and individual levels, environmental decision-making often requires the quantification of management-relevant, population-level outcomes. In this study, we generalized a modeling framework to evaluate population-level ecological risk of environmental stress and bioactive chemicals. The modeling framework includes (1) a biological model module that incorporates complex and interacting biological and ecological processes, and environmental stochasticity, (2) an effect module that links the impacts of environmental changes and chemical exposure to individual characteristics, and (3) a population module that makes decisions on the choice of population-level properties to best capture the effects and thus to track in the model based on the target species and the research and management interest. This framework is a 3-module procedure that provides an alternative way for researchers to organize, present and communicate the risk assessment modeling studies. To demonstrate this framework, we used a socioeconomically important riverine fish species, smallmouth bass Micropterus dolomieu, as the model species. We developed an individual-based model as the biological model module. We evaluated the impacts of changing water temperature and flow regimes, and the impacts of exposure to estrogenic endocrine disrupting compounds (EEDC) on smallmouth bass populations in the Chesapeake Bay Watershed, USA. Warm summer water temperatures and year-round high flows had the most severe impacts on the smallmouth bass population. An increase in exposure level to EEDC, both year-round and in summer months, substantially reduced population size, spawner and recruit abundance, and the proportion of quality-length individuals. Acute exposure to EEDC was more detrimental to the population than chronic exposure. Acute exposure during spawning season had the most severe impacts. This modeling framework can be extended to other species, environmental factors and chemicals, and can be used to inform management and conservation decisions.
Telemetry of acoustically tagged bigheaded carp (i.e., bighead carp Hypophthalmichthys nobilis and silver carp H. molitrix) and surrogate fish species has become an invaluable tool in management for these species in the upper Illinois Waterway Systems (i.e., upper Illinois River, lower Des Plaines River, and Chicago Area Waterway System). For example, movement probabilities between adjacent navigation pools need to be estimated to parameterize the Spatially Explicit Asian Carp Population Model (SEAcarP). SEAcarP is a population model used in scenario planning by the Monitoring and Response Workgroup (MRWG) to evaluate alternative management actions. These movement probabilities are estimated from the telemetry data obtained from a longitudinal network of strategically placed receivers that detect bigheaded carp that have been implanted with acoustic transmitters. In addition, fish removal by contracted fishers has become the primary method of controlling bigheaded carp in the upper Illinois and lower Des Plaines Rivers. Variable patterns in bigheaded carp distribution, habitat, and movement, influenced by seasonal and environmental conditions, make targeting bigheaded carp for removal and containment challenging and costly. Understanding these movement patterns for bigheaded carp through modeling and real-time telemetry applications informs removal efforts and facilitates monitoring and contingency actions based on fish movements.
To develop a better understanding of fish movement dynamics to meet management objectives, an existing network of real-time and data-logging acoustic receivers in the upper Illinois Waterway Systems is collaboratively managed by a multi-agency team (see Participating Agencies section above). A Telemetry Workgroup has been established by the MRWG to ensure that the multi-agency telemetry efforts are coordinated to efficiently and effectively meet the MRWG goals. This workgroup plans and executes the placement of receivers, tagging of bigheaded carp with acoustic tags, and management of the telemetry data. Three primary objectives to meet MRWG goals identified by the Telemetry Workgroup included (1) development of a common standardized telemetry database with visualization and analysis tools, (2) transitioning from Program MARK (http://www.phidot.org/software/mark/) to a custom Bayesian multi-state model for estimating movement probabilities needed for SEAcarP and (3) deploying, maintaining, and serving data from real-time acoustic receivers to inform contingency planning and fish removal.
A telemetry database and visualization tools (FishTracks) will facilitate standardization, archiving, sharing, quality assurance, visualization and analysis of the telemetry data needed for management. Modifications and additions to FishTracks will facilitate more problem-free use of the database and associated applications, as well as useful extraction of information to meet management goals. The transition to a custom Bayesian multi-state model to estimate movement probabilities will support more efficient, effective, and robust population modeling with SEAcarP by overcoming short comings of Program MARK for this purpose. These shortcomings include lack of customizability and extensibility, problems of singularities and poor-convergence, software crashes, parameter exclusion from models, an inability to consistently generate estimates of movement probability, and a lack of uncertainty estimates for movement probabilities. A real-time receiver network that is maintained and tested annually will ensure reliability and accuracy of the real-time alerts to bigheaded carp movements that can be used by management to plan contingency actions.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Interim summary report 2020","largerWorkSubtype":{"id":3,"text":"Organization Series"},"language":"English","publisher":"Asian Carp Regional Coordinating Committee","usgsCitation":"Knights, B.C., Brey, M.K., Stanton, J.C., Harrison, T.J., Appel, D., Hlavacek, E., and Duncker, J.J., 2020, USGS Telemetry Project: Interim Summary Report, 6 p.","productDescription":"6 p.","startPage":"41","endPage":"46","ipdsId":"IP-128212","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":391250,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://invasivecarp.us/PlansReports.html"},{"id":426889,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois","otherGeospatial":"upper Illinois Waterway","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -87.34655836479192,\n 42.36183831544582\n ],\n [\n -89.74494117447833,\n 42.36183831544582\n ],\n [\n -89.74494117447833,\n 40.51767088873834\n ],\n [\n -87.34655836479192,\n 40.51767088873834\n ],\n [\n -87.34655836479192,\n 42.36183831544582\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Knights, Brent C. 0000-0001-8526-8468 bknights@usgs.gov","orcid":"https://orcid.org/0000-0001-8526-8468","contributorId":2906,"corporation":false,"usgs":true,"family":"Knights","given":"Brent","email":"bknights@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":826139,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brey, Marybeth K. 0000-0003-4403-9655 mbrey@usgs.gov","orcid":"https://orcid.org/0000-0003-4403-9655","contributorId":187651,"corporation":false,"usgs":true,"family":"Brey","given":"Marybeth","email":"mbrey@usgs.gov","middleInitial":"K.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":826140,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stanton, Jessica C. 0000-0002-6225-3703 jcstanton@usgs.gov","orcid":"https://orcid.org/0000-0002-6225-3703","contributorId":5634,"corporation":false,"usgs":true,"family":"Stanton","given":"Jessica","email":"jcstanton@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":826141,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Harrison, Travis J. 0000-0002-9195-738X","orcid":"https://orcid.org/0000-0002-9195-738X","contributorId":213966,"corporation":false,"usgs":true,"family":"Harrison","given":"Travis","email":"","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":826142,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Appel, Douglas 0000-0001-8775-1058","orcid":"https://orcid.org/0000-0001-8775-1058","contributorId":268159,"corporation":false,"usgs":true,"family":"Appel","given":"Douglas","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":826143,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hlavacek, Enrika 0000-0002-9872-2305 ehlavacek@usgs.gov","orcid":"https://orcid.org/0000-0002-9872-2305","contributorId":149114,"corporation":false,"usgs":true,"family":"Hlavacek","given":"Enrika","email":"ehlavacek@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":826144,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Duncker, James J. 0000-0001-5464-7991 jduncker@usgs.gov","orcid":"https://orcid.org/0000-0001-5464-7991","contributorId":4316,"corporation":false,"usgs":true,"family":"Duncker","given":"James","email":"jduncker@usgs.gov","middleInitial":"J.","affiliations":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true},{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true},{"id":35680,"text":"Illinois-Iowa-Missouri Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826145,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70229995,"text":"70229995 - 2020 - Book review: Rare earth element resources: Indian context","interactions":[],"lastModifiedDate":"2022-03-23T14:29:13.039768","indexId":"70229995","displayToPublicDate":"2020-12-01T09:27:08","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1472,"text":"Economic Geology","active":true,"publicationSubtype":{"id":10}},"title":"Book review: Rare earth element resources: Indian context","docAbstract":"Rare Earth Element Resources: Indian Context. Yamuna Singh. 2020. ISBN 978-3-030-41353-8. Society of Earth Scientists Series, Springer, Cham, Switzerland, 269 Pp. Hardcover and eBook. €93.08
Rare Earth Element Resources: Indian Context by Yamuna Singh provides an excellent review of rare earth element (REE) deposits and occurrences in India with an emphasis on placer deposits, India’s most notable REE resource. This 269-page, 10-chapter book not only describes REE occurrences but also provides introductory material on REE geochemistry and discusses other potential industrial sources including recycling, fly ash, and mine waste products. The book concludes with a chapter on the state of the REE industry in India and discusses possible future trends and needs. Anyone interested in exploring for REEs in India will find this book from Springer’s Society of Earth Scientists series a useful reference.
In most forest systems, the dynamics of forest canopy gap development play an important role in the transition from relatively short-lived early successional tree species to longer-lived, late successional tree species. In resilient forest systems, tree seedlings establish within newly created canopy gaps and grow to close the gap within one or two decades of disturbance. However, evidence in portions of the Upper Mississippi River System indicates that floodplain forests do not appear to be following these same trajectories, with canopy gaps instead seeming to fail to recruit new tree seedlings and reverting to non-forested cover types. Because of the heavy dominance of short-lived tree species in current UMRS forests, there is concern that continued failure of canopy gaps to recruit back to forest could be an early indicator of long-term, widespread forest loss as gaps become larger and larger and begin to coalesce into large, non-forested areas. Little research to date has documented either the density and distribution of forest canopy gaps across the UMRS or the vegetative conditions within those gaps to provide an initial assessment of forest dynamics in those areas. The current study utilizes both remotely sensed data and field sampling to assess the conditions of forest canopy gaps within 6 navigation pools on the Upper Mississippi River and one pool on the Illinois River. In general, canopy gap distributions and characteristics are similar across the study, with most pools ranging from 3% to 5% of forest canopy in gaps. Gap sizes are also relatively uniform, with most pools averaging 0.09 to 0.14 ha per gap. The highest proportion of forest cover in canopy gaps at the pool level was driven by the total number of gaps and not gap size, indicating that canopy gap formation in this system is commonly due to individual tree or small clump mortality. Undesirable competing vegetation was dominant in most canopy gaps, with reed canarygrass and native forbs being most prevalent in upper pools and vines most problematic in the lower pools. In the upper pools, very little viable forest regeneration is occurring within canopy gaps. The viability of forest regeneration increases in middle and lower pools, though competing vegetation continues to be a problem. Overall, canopy gaps appear 3 most likely to recruit back to forest in lower pools, and chronic forest loss facilitated by regeneration failures seems most likely in upper pools. However, competing vegetation in lower pools may still interact with woody regeneration to limit effective reestablishment of forest canopy.
","language":"English","publisher":"Long Term Resource Monitoring (LTRM); U.S. Army Corps of Engineers (USACE)","usgsCitation":"Guyon, L.J., Strassman, A.C., Oines, A., Meier, A.R., Thomsen, M., Sattler, S., De Jager, N.R., Hoy, E.E., Vandermyde, B.J., and Cosgriff, R.J., 2020, Forest canopy gap dynamics: Quantifying forest gaps and understanding gap – level forest regeneration in Upper Mississippi River floodplain forests: Completion Report SOW2019FG5, 73 p.","productDescription":"73 p.","ipdsId":"IP-150741","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":426039,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://umesc.usgs.gov/data_library/ltrmp_other/LTRMScienceInSupportOfRestoration_SOW2019FG5_ForestGapStudy_CompletionReport_20201229.pdf"},{"id":426061,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Iowa, Minnesota, Missouri, Wisconsin","otherGeospatial":"upper Mississippi River floodplain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -94.16929677532707,\n 45.5117015671573\n ],\n [\n -94.03051125623395,\n 39.87297631602874\n ],\n [\n -89.76859048524918,\n 36.34954833530216\n ],\n [\n -87.91880978061673,\n 37.58007438726108\n ],\n [\n -88.11043642902897,\n 41.940368108659555\n ],\n [\n -90.1957960672068,\n 44.06021292293801\n ],\n [\n -93.45388126834942,\n 46.00963294383138\n ],\n [\n -94.16929677532707,\n 45.5117015671573\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Guyon, Lyle J.","contributorId":215690,"corporation":false,"usgs":false,"family":"Guyon","given":"Lyle","email":"","middleInitial":"J.","affiliations":[{"id":36894,"text":"Illinois Natural History Survey","active":true,"usgs":false}],"preferred":false,"id":895489,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Strassman, Andrew C. 0000-0002-9792-7181 astrassman@usgs.gov","orcid":"https://orcid.org/0000-0002-9792-7181","contributorId":4575,"corporation":false,"usgs":true,"family":"Strassman","given":"Andrew","email":"astrassman@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":895490,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Oines, Alexandra","contributorId":334393,"corporation":false,"usgs":false,"family":"Oines","given":"Alexandra","email":"","affiliations":[{"id":61757,"text":"Winona State University","active":true,"usgs":false}],"preferred":false,"id":895491,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Meier, Andrew R.","contributorId":215691,"corporation":false,"usgs":false,"family":"Meier","given":"Andrew","email":"","middleInitial":"R.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":895492,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thomsen, Meredith","contributorId":197064,"corporation":false,"usgs":false,"family":"Thomsen","given":"Meredith","affiliations":[],"preferred":false,"id":895493,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sattler, Stepahnie R 0000-0003-4417-2480","orcid":"https://orcid.org/0000-0003-4417-2480","contributorId":299008,"corporation":false,"usgs":false,"family":"Sattler","given":"Stepahnie R","affiliations":[{"id":48800,"text":"Former USGS, UMESC employee","active":true,"usgs":false}],"preferred":false,"id":895494,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"De Jager, Nathan R. 0000-0002-6649-4125 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J.","contributorId":215693,"corporation":false,"usgs":false,"family":"Vandermyde","given":"Benjamin","email":"","middleInitial":"J.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":895497,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Cosgriff, Robert J.","contributorId":215692,"corporation":false,"usgs":false,"family":"Cosgriff","given":"Robert","email":"","middleInitial":"J.","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":895498,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70228975,"text":"70228975 - 2020 - A multispecies approach to manage effects of land cover and weather on upland game birds","interactions":[],"lastModifiedDate":"2022-02-25T15:21:38.85868","indexId":"70228975","displayToPublicDate":"2020-12-01T09:11:55","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"A multispecies approach to manage effects of land cover and weather on upland game birds","docAbstract":"Loss and degradation of grasslands in the Great Plains region have resulted in major declines in abundance of grassland bird species. To ensure future viability of grassland bird populations, it is crucial to evaluate specific effects of environmental factors among species to determine drivers of population decline and develop effective conservation strategies. We used threshold models to quantify the effects of land cover and weather changes in \"lesser prairie-chicken\" and \"greater prairie-chicken\" (Tympanuchus pallidicinctus and T. cupido, respectively), northern bobwhites (Colinus virginianus), and ring-necked pheasants (Phasianus colchicus). We demonstrated a novel approach for estimating landscape conditions needed to optimize abundance across multiple species at a variety of spatial scales. Abundance of all four species was highest following wet summers and dry winters. Prairie chicken and ring-necked pheasant abundance was highest following cool winters, while northern bobwhite abundance was highest following warm winters. Greater prairie chicken and northern bobwhite abundance was also highest following cooler summers. Optimal abundance of each species occurred in landscapes that represented a grassland and cropland mosaic, though prairie chicken abundance was optimized in landscapes with more grassland and less edge habitat than northern bobwhites and ring-necked pheasants. Because these effects differed among species, managing for an optimal landscape for multiple species may not be the optimal scenario for any one species.
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