Climate change is altering the spatial distribution of many species around the world. In response, we need to identify and protect suitable areas for a large proportion of the fauna so that they persist through time. This exercise must also evaluate the ability of existing protected areas to provide safe havens for species in the context of climate change. Here, we combined passive acoustic monitoring, semi-automatic species identification models, and species distribution models of 21 bird and frog species based on past (1980–1989), present (2005–2014), and future (2040–2060) climate scenarios to determine how species distributions relate to the current distribution of protected areas in Puerto Rico. Species detection/non-detection data were acquired across ~ 700 sampling sites. We developed always-suitable maps that characterized suitable habitats in all three time periods for each species and overlaid these maps to identify regions with high species co-occurrence. These distributions were then compared with the distribution of existing protected areas. We show that Puerto Rico is projected to become dryer by 2040–2060, and precipitation in the warmest quarter was among the most important variables affecting bird and frog distributions. A large portion of always-suitable areas (ASA) is outside of protected areas (> 80%), and the percent of protected areas that overlaps with always-suitable areas is larger for bird (75%) than frog (39%) species. Our results indicate that present protected areas will not suffice to safeguard bird and frog species under climate change; however, the establishment of larger protected areas, buffer zones, and connectivity between protected areas may allow species to find suitable niches to withstand environmental changes.
","language":"English","publisher":"Springer Link","doi":"10.1007/s10531-021-02258-9","usgsCitation":"Campos-Cerqueira, M., Terando, A., Murray, B., Collazo, J.A., and Aide, M., 2021, Climate change is creating a mismatch between protected areas and suitable habitats for frogs and birds in Puerto Rico: Biological Conservation, v. 30, p. 3509-3528, https://doi.org/10.1007/s10531-021-02258-9.","productDescription":"20 p.","startPage":"3509","endPage":"3528","ipdsId":"IP-123023","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":40926,"text":"Southeast Climate Adaptation Science Center","active":true,"usgs":true}],"links":[{"id":451394,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10531-021-02258-9","text":"Publisher Index Page"},{"id":387778,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Puerto Rico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.3077392578125,\n 17.913409288694826\n ],\n [\n -65.58837890625,\n 17.913409288694826\n ],\n [\n -65.58837890625,\n 18.552532366385577\n ],\n [\n -67.3077392578125,\n 18.552532366385577\n ],\n [\n -67.3077392578125,\n 17.913409288694826\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"30","noUsgsAuthors":false,"publicationDate":"2021-07-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Campos-Cerqueira, Marconi","contributorId":261906,"corporation":false,"usgs":false,"family":"Campos-Cerqueira","given":"Marconi","email":"","affiliations":[{"id":53077,"text":"Rainforest Connection","active":true,"usgs":false}],"preferred":false,"id":820742,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Terando, Adam 0000-0002-9280-043X","orcid":"https://orcid.org/0000-0002-9280-043X","contributorId":205908,"corporation":false,"usgs":true,"family":"Terando","given":"Adam","affiliations":[{"id":565,"text":"Southeast Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":820743,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Murray, Brent","contributorId":261907,"corporation":false,"usgs":false,"family":"Murray","given":"Brent","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":820744,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Collazo, Jaime A. 0000-0002-1816-7744","orcid":"https://orcid.org/0000-0002-1816-7744","contributorId":217287,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime","email":"","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":820745,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Aide, Mitchell","contributorId":261908,"corporation":false,"usgs":false,"family":"Aide","given":"Mitchell","email":"","affiliations":[{"id":38462,"text":"University of Puerto Rico","active":true,"usgs":false}],"preferred":false,"id":820746,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70223149,"text":"70223149 - 2021 - Evaluation of a modified rapid viability-polymerase chain reaction method for Bacillus atrophaeus spores in water matrices","interactions":[],"lastModifiedDate":"2021-08-12T12:33:45.714578","indexId":"70223149","displayToPublicDate":"2021-07-27T07:32:46","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2390,"text":"Journal of Microbiological Methods","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of a modified rapid viability-polymerase chain reaction method for Bacillus atrophaeus spores in water matrices","docAbstract":"A rapid method that provides information on the viability of organisms is needed to protect public health and ensure that remediation efforts following a release of a biological agent are effective. The rapid viability-polymerase chain reaction (RV-PCR) method combines broth culture and molecular methods to provide results on whether viable organisms are present in less than 15 h. In this study, a modified RV-PCR (mRV-PCR) method was compared to a membrane-filtration culture method for the detection of viable Bacillus spores in water matrices. Samples included small and large volumes of chlorine and non‑chlorine treated tap water. Large volume water samples (up to 100 L), were processed by ultrafiltration using a semi-automated waterborne pathogen concentrator, followed by centrifugation as a secondary concentration technique. The concentrated samples were analyzed by mRV-PCR and culture methods. The overall agreement between the mRV-PCR and culture methods when seed concentrations were greater than 10 spores per sample volume analyzed was 96%. The total time from the start of sample processing to the final sample result for the mRV-PCR method was decreased by approximately 2 h, in comparison to the previously published RV-PCR method because of the incorporation of shorter, more efficient primary and secondary concentration steps and a shorter DNA extraction technique. Overall, this study confirmed that RV-PCR is a promising approach for identifying viable Bacillus spores in small- and large-volume water samples and for producing results in less time than traditional culture methods.
Fault friction is central to understanding earthquakes, yet laboratory rock mechanics experiments are restricted to, at most, meter scale. Questions thus remain as to the applicability of measured frictional properties to faulting in situ. In particular, the slip-weakening distance
This article develops global models of damping scaling factors (DSFs) for subduction zone earthquakes that are functions of the damping ratio, spectral period, earthquake magnitude, and distance. The Next Generation Attenuation for subduction earthquakes (NGA-Sub) project has developed the largest uniformly processed database of recorded ground motions to date from seven subduction regions: Alaska, Cascadia, Central America and Mexico, South America, Japan, Taiwan, and New Zealand. NGA-Sub used this database to develop new ground motion models (GMMs) at a reference 5% damping ratio. We worked with the NGA-Sub project team to develop an extended database that includes pseudo-spectral accelerations (PSA) for 11 damping ratios between 0.5% and 30%. We use this database to develop parametric models of DSF for both interface and intraslab subduction earthquakes that can be used to adjust any subduction GMM from a reference 5% damping ratio to other damping ratios. The DSF is strongly influenced by the response spectral shape and the duration of motion; therefore, in addition to the damping ratio, the median DSF model uses spectral period, magnitude, and distance as surrogate predictor variables to capture the effects of the spectral shape and the duration of motion. We also develop parametric models for the standard deviation of DSF. The models presented in this article are for the RotD50 horizontal component of PSA and are compared with the models for shallow crustal earthquakes in active tectonic regions. Some noticeable differences arise from the considerably longer duration of interface records for very large magnitude events and the enriched high-frequency content of intraslab records, compared with shallow crustal earthquakes. Regional differences are discussed by comparing the proposed global models with the data from each subduction region along with recommendations on the applicability of the models.
Understanding interfacial reactions that occur between the active layer and charge-transport layers can extend the stability of perovskite solar cells. In this study, the exposure of methylammonium lead iodide (CH3NH3PbI3) thin films prepared on poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS)-coated glass to 70% relative humidity (R.H.) leads to a perovskite crystal structure change from tetragonal to cubic within 2 days. Interface-sensitive photoluminescence measurements indicate that the structural change originates at the PEDOT:PSS/perovskite interface. During exposure to 30% R.H., the same structural change occurs over a much longer time scale (>200 days), and a reflection consistent with the presence of (CH3)2NH2PbI3 is detected to coexist with the cubic phase by X-ray diffraction pattern. The authors propose that chemical interactions at the PEDOT:PSS/perovskite interface, facilitated by humidity, promote the formation of dimethylammonium, (CH3)2NH2+. The partial A-site substitution of CH3NH3+ for (CH3)2NH2+ to produce a cubic (CH3NH3)1−x[(CH3)2NH2]xPbI3 phase explains the structural change from tetragonal to cubic during short-term humidity exposure. When (CH3)2NH2+ content exceeds its solubility limit in the perovskite during longer humidity exposures, a (CH3)2NH2+-rich, hexagonal phase of (CH3NH3)1−x[(CH3)2NH2]xPbI3 emerges. These interfacial interactions may have consequences for device stability and performance beyond CH3NH3PbI3 model systems and merit close attention from the perovskite research community.
The Wyoming Landscape Conservation Initiative (WLCI) was established in 2007 as a collaborative interagency partnership to develop and implement science-based conservation actions. During the past 11 years, partners from U.S. Geological Survey (USGS), State and Federal land management agencies, universities, and the public have collaborated to implement a long-term (more than 10 years) science-based program that assesses and enhances the quality and quantity of wildlife habitats in the southwest Wyoming region while facilitating responsible development. The USGS WLCI Science Team completes scientific research and develops tools that inform and support WLCI partner planning, decision making, and on-the-ground management actions.
In fiscal year 2018, the USGS initiated 3 new projects and continued efforts on 21 ongoing science and web-development projects. The first new project was initiated to support Secretarial Order 3362 which calls on the USGS to assist Western States in mapping big-game migration corridors and developing new mapping tools. During 2018, the USGS hosted a workshop in Laramie, Wyoming, which included more than 70 State and Federal wildlife experts from Colorado, New Mexico, Texas, and Wyoming. Most of the mapping and migration tool curricula used in the workshop were derived from prior WLCI studies and mapping efforts of big-game migration movement in habitats undergoing large-scale energy development.
The second new project was in response for WLCI partners to better understand sedimentation and hydrogeomorphic processes in a cold-desert headwater and the third new project was designed to improve our approach for people to access, manage, and analyze WLCI data and WLCI resource information. The USGS published 18 products (including peer-reviewed journal articles, USGS series publications, and data releases) and provided more than a dozen professional oral and poster presentations at scientific meetings and numerous informal presentations to WLCI partners at meetings and workshops. This report summarizes the objectives and status of each project and highlights the USGS 2018 accomplishments and products.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211067","usgsCitation":"Anderson, P.J., Aldridge, C.L., Alexander, J.S., Assal, T.J., Aulenbach, S., Bowen, Z.H., Chalfoun, A.D., Chong, G.W., Copeland, H., Edmunds, D.R., Germaine, S., Graves, T., Heinrichs, J.A., Homer, C.G., Huber, C.C., Johnston, A., Kauffman, M.J., Manier, D.J., McShan, R.R., Eddy-Miller, C.A., Miller, K.A., Monroe, A.P., O’Donnell, M.S., Ortega, A., Walters, A.W., Wieferich, D., Wyckoff, T.B., and Zeigenfuss, L., 2020, U.S. Geological Survey science for the Wyoming Landscape Conservation Initiative—2018 annual report: U.S. Geological Survey Open-File Report 2021–1067, 33 p., https://doi.org/10.3133/ofr20211067.","productDescription":"ix, 33 p.","onlineOnly":"Y","ipdsId":"IP-117398","costCenters":[{"id":207,"text":"Core Research Center","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":464,"text":"Nebraska Water Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":387403,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1067/coverthb.jpg"},{"id":387404,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1067/ofr20211067.pdf","text":"Report","size":"6.37 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1067"}],"country":"United States","state":"Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.060791015625,\n 43.11702412135048\n ],\n [\n -111.060791015625,\n 41.02964338716638\n ],\n [\n -105.677490234375,\n 41.0130657870063\n ],\n [\n -105.875244140625,\n 41.60722821271717\n ],\n [\n -105.83129882812499,\n 42.32606244456202\n ],\n [\n -106.10595703125,\n 42.50450285299051\n ],\n [\n -107.083740234375,\n 42.49640294093705\n ],\n [\n -107.940673828125,\n 42.52879629320373\n ],\n [\n -108.896484375,\n 42.49640294093705\n ],\n [\n -109.3798828125,\n 42.62587560259137\n ],\n [\n -109.75341796875,\n 42.956422511073335\n ],\n [\n -110.06103515625,\n 43.197167282501276\n ],\n [\n -111.060791015625,\n 43.11702412135048\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Building C
Fort Collins, CO 80526-8118
This report addresses system characterization of Planet’s Dove Classic satellites and is part of a series of system characterization reports produced and delivered by the U.S. Geological Survey Earth Resources Observation and Science Cal/Val Center of Excellence. These reports present and detail the methodology and procedures for characterization; present technical and operational information about the specific sensing system being evaluated; and provide a summary of test measurements, data retention practices, data analysis results, and conclusions.
Since 2013, Planet has launched more than 360 Dove 3U CubeSats, where U stands for 10-centimeter (cm) x 10-cm x 10-cm stowed dimensions, each weighing about 5 kilograms. Since 2015, all Dove satellites have had four-band imagers with about a 4-meter (m) pixel ground sample distance. Since 2016, all Doves have been launched into Sun-synchronous orbits varying from 474 to 524 kilometers, with inclinations between 97 and 98 degrees. The Dove series satellites do not have orbit maintenance capabilities; thus, their orbits decay slowly over time, contributing to shorter lifetimes of about 3 years. More information on Planet satellites and sensors is available in the “2020 Joint Agency Commercial Imagery Evaluation—Remote Sensing Satellite Compendium” and from the manufacturer at https://www.planet.com/.
The Earth Resources Observation and Science Cal/Val Center of Excellence system characterization team completed data analyses to characterize the geometric (interior and exterior), radiometric, and spatial performances. Results of these analyses indicate that Dove Classic has an interior geometric performance in the range of −0.218 (−0.073 pixel) to −0.037 m (−0.012 pixel) in easting and −0.167 (−0.056 pixel) to −0.111 m (−0.037 pixel) in northing in band-to-band registration, an exterior geometric error of −6.841 (−2.280 pixels) in easting and −6.235 m (−2.078 pixels) in northing offset in comparison to Landsat 8 Operational Land Imager, a radiometric performance in the range of −0.057 to −0.010 in offset and 0.963 to 1.298 in slope, and a spatial performance in the range of 2.77 to 3.35 pixels for full width at half maximum, with a modulation transfer function at a Nyquist frequency in the range of 0.003 to 0.010.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211030C","usgsCitation":"Kim, M., Park, S., Anderson, C., and Stensaas, G.L., 2021, System characterization report on Planet’s Dove Classic, chap. C of Ramaseri Chandra, S.N., comp., System characterization of Earth observation sensors: U.S. Geological Survey Open-File Report 2021–1030, 28 p., https://doi.org/10.3133/ofr20211030C.","productDescription":"v, 28 p.","numberOfPages":"38","onlineOnly":"Y","ipdsId":"IP-126677","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":387443,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1030/c/ofr20211030c.pdf","text":"Report","size":"31.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021–1030C"},{"id":387442,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1030/c/coverthb.jpg"}],"contact":"Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198
A study was conducted during June–October 2020 to evaluate factors affecting the migration success of adult sockeye salmon (Oncorhynchus nerka) in the Yakima River, Washington. A total of 144 adult sockeye salmon were tagged and released during the study. Most fish (112 fish) were collected, tagged with passive integrated transponder (PIT), and released at the mouth of the Yakima River. The remaining fish were tagged with a radio transmitter and PIT tag: 13 fish were collected, tagged, and released at Prosser Dam; 13 fish were collected and tagged at Prosser Dam, transported downstream, and released at the mouth of the Yakima River; and 6 fish were collected, tagged, and released at the mouth of the Yakima River. Radio-tagged fish released at Prosser Dam initially moved upstream and spread out in the river reach between Prosser and Sunnyside Dams, but all fish stopped moving and several transmitters were recovered. Detection records and temperature data from recovered transmitters were the basis for inferring that avian predators consumed at least 6 of the 13 fish. Fifteen of the 19 radio-tagged sockeye salmon released at the mouth of the Yakima River moved upstream in the Columbia River and were detected at Johnson Island in the Hanford Reach, or at Priest Rapids Dam. Two of these fish, tagged on August 7, eventually moved back downstream and entered the Yakima River when water temperatures in the lower Yakima River were 16–18 degrees Celsius (°C). One fish moved upstream to Sunnyside Dam where its tag was later recovered. The other fish moved farther upstream and was detected at Prosser Dam, but eventually moved downstream and its tag was recovered near Benton City, Washington. None of the recovered tags were found near a carcass. More than one-half of the sockeye salmon that were collected, tagged, and released at the mouth of the Yakima River were subsequently detected, and the greatest proportion of fish from groups released during June, July, and August entered the Yakima River. This finding suggests that adult sockeye salmon are present at the mouth of the Yakima River throughout the summer. Detection records for tagged fish at monitoring sites located near cool water inputs in the lower Yakima River suggest that sockeye salmon do not spend a substantial amount of time at these locations. Fish count data at Prosser Dam fish ladders showed that sockeye salmon had a bi-modal pattern of upstream migration with peaks in late June/early July and September when water temperature in the lower Yakima River was 20 °C or less. Sixty-one percent of PIT-tagged sockeye salmon detected at Prosser Dam were eventually collected at the adult fish trapping facility at Roza Dam where fish are collected and transported upstream to Cle Elum Reservoir. These data, in conjunction with results from other studies, suggest that a substantial proportion of Yakima River sockeye salmon fail to arrive at Roza Dam. Additional research will be required to better understand factors affecting Yakima River sockeye salmon.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211075","collaboration":"Prepared in cooperation with Bureau of Reclamation, Yakama Nation Fisheries, and Washington Department of Fish and Wildlife","usgsCitation":"Kock, T.J., Hansen, A.C., Evans, S.D., Visser, R., Saluskin, B., Matala, A., and Hoffarth, P., 2021, Evaluation of factors affecting migration success of adult sockeye salmon (Oncorhynchus nerka) in the Yakima River, Washington, 2020: U.S. Geological Survey Open-File Report 2021–1075, 30 p., https://doi.org/10.3133/ofr20211075.","productDescription":"vi, 30 p.","onlineOnly":"Y","ipdsId":"IP-128700","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":387441,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1075/ofr20211075.pdf","text":"Report","size":"5.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1075"},{"id":387440,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1075/coverthb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Yakima River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.8770751953125,\n 45.96642454131025\n ],\n [\n -118.67431640625,\n 45.96642454131025\n ],\n [\n -118.67431640625,\n 46.916503267244835\n ],\n [\n -120.8770751953125,\n 46.916503267244835\n ],\n [\n -120.8770751953125,\n 45.96642454131025\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115-5016
1. Migratory waterfowl facilitate long distance dispersal of zoonotic pathogens and are increasingly recognized as contributing to the geographic spread of avian influenza viruses (AIV). AIV are globally distributed and have the potential to produce highly contagious poultry disease, economically impact both large-scale and backyard poultry producers, and raise the specter of epidemics and pandemics in human populations.
2. Because migratory waterfowl behavior varies across multiple spatial and temporal scales, the timing and distribution of wild bird AIV introductions to poultry are also heterogeneous in time and space. To help reduce economic impacts to the poultry industry and enable poultry producers to better anticipate when and where poultry outbreaks may occur, it is critically important to consider the movement ecology of the waterfowl species transporting and transmitting AIV.
3. We used telemetry for a geographically widespread and common AIV host, blue-winged teal (Spatula discors; BWTE), to model reservoir host movement states with respect to backyard and commercial poultry facilities in the United States. Our modeling framework enabled us to estimate wild bird proximity to poultry facilities while concurrently assessing the influence of poultry facilities on BWTE movement state transition. Our primary objective was to estimate the likelihood of duck and poultry overlap by estimating when and where BWTE were geographically closest to poultry.
4. Synthesis and applications. Migratory waterfowl facilitate dispersal of the avian influenza viruses that cause highly contagious poultry disease. Movement analysis of blue-winged teal indicates that spatio-temporal overlap between wild birds and poultry facilities varies by season, the poultry type produced (e.g., turkey, chicken), and if the facility is a commercial or backyard operation. These findings are broadly applicable to disease ecology research and can be applied by poultry producers to improve bio-security, enhance poultry management, and prioritize disease surveillance efforts.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2664.13963","usgsCitation":"Humphreys, J.M., Douglas, D.C., Ramey, A.M., Mullinax, J.M., Soos, C., Link, P.T., Walther, P., and Prosser, D., 2021, The spatial-temporal relationship of blue-winged teal to domestic poultry: Movement state modeling of a highly mobile avian influenza host: Journal of Applied Ecology, v. 58, no. 10, p. 2040-2052, https://doi.org/10.1111/1365-2664.13963.","productDescription":"13 p.","startPage":"2040","endPage":"2052","ipdsId":"IP-118863","costCenters":[{"id":107,"text":"Alaska Climate Science Center","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":451405,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2664.13963","text":"Publisher Index Page"},{"id":387599,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"58","issue":"10","noUsgsAuthors":false,"publicationDate":"2021-08-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Humphreys, John M.","contributorId":217932,"corporation":false,"usgs":false,"family":"Humphreys","given":"John","email":"","middleInitial":"M.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":820044,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Douglas, David C. 0000-0003-0186-1104 ddouglas@usgs.gov","orcid":"https://orcid.org/0000-0003-0186-1104","contributorId":2388,"corporation":false,"usgs":true,"family":"Douglas","given":"David","email":"ddouglas@usgs.gov","middleInitial":"C.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":820046,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ramey, Andrew M. 0000-0002-3601-8400 aramey@usgs.gov","orcid":"https://orcid.org/0000-0002-3601-8400","contributorId":1872,"corporation":false,"usgs":true,"family":"Ramey","given":"Andrew","email":"aramey@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":820045,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mullinax, Jennifer M.","contributorId":221170,"corporation":false,"usgs":false,"family":"Mullinax","given":"Jennifer","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":820047,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Soos, Catherine","contributorId":177909,"corporation":false,"usgs":false,"family":"Soos","given":"Catherine","email":"","affiliations":[],"preferred":false,"id":820048,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Link, Paul T.","contributorId":53611,"corporation":false,"usgs":false,"family":"Link","given":"Paul","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":820049,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Walther, Patrick","contributorId":213915,"corporation":false,"usgs":false,"family":"Walther","given":"Patrick","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":820050,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Prosser, Diann 0000-0002-5251-1799","orcid":"https://orcid.org/0000-0002-5251-1799","contributorId":217931,"corporation":false,"usgs":true,"family":"Prosser","given":"Diann","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":820051,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70223494,"text":"70223494 - 2021 - Water–rock interaction and the concentrations of major, trace, and rare earth elements in hydrocarbon-associated produced waters of the United States","interactions":[],"lastModifiedDate":"2024-09-16T16:35:32.635392","indexId":"70223494","displayToPublicDate":"2021-07-26T07:46:46","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9161,"text":"Environmental Science: Processes & Impacts","active":true,"publicationSubtype":{"id":10}},"title":"Water–rock interaction and the concentrations of major, trace, and rare earth elements in hydrocarbon-associated produced waters of the United States","docAbstract":"Studies of co-produced waters from hydrocarbon extraction across multiple energy-producing basins have generally focused on major ions or a few select tracers, and studies that examine trace elements and involve laboratory experiments have generally been basin specific. Here, new perspective is sought through a broad analysis of concentration data for 26 elements from three hydrocarbon well types using the U.S. Geological Survey National Produced Waters Geochemical Database (v2.3). Those data are compared to leachates (water, hydrochloric acid, and artificial brine) from 12 energy-resource related shales from across the United States. Both lower pH and higher ionic strength were associated with greater concentrations of many trace elements in produced waters. However, individual effects were difficult to distinguish because higher ionic strengths drive decreases in pH. Water–rock interactions in the leaching experiments generally replicated produced water concentrations for trace elements including Al, As, Cd, Co, Cu, Mo, Ni, Pb, Sb, Si, and Zn. Enhanced middle rare earth element (REE) mobilization relative to shale REE content occurred with low pH leachates. Produced water concentrations of Li, Sr, and Ba were not replicated by the leaching experiments. Patterns of high Li, Sr, and Ba concentrations and ratios relative to other elements across produced waters types indicate controls on these elements in many settings related to pore space pools of salts, brines, and ion-exchange sites affected by diagenetic processes. The size of those pools is diluted and masked by other water–rock interaction processes at the water–rock ratios necessitated by laboratory experiments. The results broadly link water–rock interaction processes and environmental patterns across a wide variety of produced waters and host formations and thus provide context for trace element data from other environmental and laboratory studies of such waters.
The forage maturation hypothesis (FMH) states that energy intake for ungulates is maximised when forage biomass is at intermediate levels. Nevertheless, metabolic allometry and different digestive systems suggest that resource selection should vary across ungulate species. By combining GPS relocations with remotely sensed data on forage characteristics and surface water, we quantified the effect of body size and digestive system in determining movements of 30 populations of hindgut fermenters (equids) and ruminants across biomes. Selection for intermediate forage biomass was negatively related to body size, regardless of digestive system. Selection for proximity to surface water was stronger for equids relative to ruminants, regardless of body size. To be more generalisable, we suggest that the FMH explicitly incorporate contingencies in body size and digestive system, with small-bodied ruminants selecting more strongly for potential energy intake, and hindgut fermenters selecting more strongly for surface water.
We studied biotic ligand model (BLM) predictions of toxicity of nickel (Ni) and zinc (Zn) in natural waters from Illinois and Minnesota USA which had combinations of pH, hardness, and dissolved organic carbon (DOC) more extreme than 99.7% of waters in a nationwide database. We conducted 7-d chronic tests with Ceriodaphnia dubia, and 96-hr acute test and 14-d chronic tests with Neocloeon triangulifer, and estimated LC50s and EC20s for both species. Toxicity of Ni and Zn to both species differed among test waters by factors from 8 (Zn tests with C. dubia) to 35 (Zn tests with N. triangulifer). For both species and metals, tests with Minnesota waters (low pH and hardness, high DOC) showed lower toxicity than Illinois waters (high pH, high hardness, low DOC). Recalibration of the Ni BLM to be more responsive to pH-related changes improved predictions of Ni toxicity, especially for C. dubia. We compared several input data scenarios for the Zn BLM, which generally had minor effects on Model Performance Scores (MPS). A scenario that included inputs of modeled dissolved inorganic carbon and measured Al and Fe(III) produced highest MPS values for tests with both C. dubia and N. triangulifer. Overall, the BLM framework successfully modeled variation in toxicity for both Zn and Ni across wide ranges of water chemistry in tests with both standard and novel test organisms.
","language":"English","publisher":"Society of Environmental Toxicology and Chemistry","doi":"10.1002/etc.5178","usgsCitation":"Besser, J.M., Ivey, C.D., Steevens, J.A., Cleveland, D.M., Soucek, D.J., Dickinson, A., Van Genderen, E.J., Ryan, A.C., Schlekat, C.E., Garman, E., Middleton, E., and Santore, R.C., 2021, Modeling the bioavailability of nickel and zinc to Ceriodaphnia dubia and Neocloeon triangulifer in toxicity tests with Natural Waters: Environmental Toxicology and Chemistry, v. 40, no. 11, p. 3049-3062, https://doi.org/10.1002/etc.5178.","productDescription":"14 p.","startPage":"3049","endPage":"3062","ipdsId":"IP-124650","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":436265,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GWJRF3","text":"USGS data release","linkHelpText":"Survival, growth and reproduction of C. dubia and N. triangulifer to nickel and zinc exposure in natural waters"},{"id":388396,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Minnesota","otherGeospatial":"Keeley Creek, Spoon Creek, Spring Creek, St. Louis River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.2080078125,\n 47.100044694025215\n ],\n [\n -91.23046875,\n 47.100044694025215\n ],\n [\n -91.23046875,\n 48.1367666796927\n ],\n [\n -93.2080078125,\n 48.1367666796927\n ],\n [\n -93.2080078125,\n 47.100044694025215\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -89.8681640625,\n 40.38002840251183\n ],\n [\n -87.5390625,\n 40.38002840251183\n ],\n [\n -87.5390625,\n 41.541477666790286\n ],\n [\n -89.8681640625,\n 41.541477666790286\n ],\n [\n -89.8681640625,\n 40.38002840251183\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"40","issue":"11","noUsgsAuthors":false,"publicationDate":"2021-07-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Besser, John M. 0000-0002-9464-2244 jbesser@usgs.gov","orcid":"https://orcid.org/0000-0002-9464-2244","contributorId":2073,"corporation":false,"usgs":true,"family":"Besser","given":"John","email":"jbesser@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":821744,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ivey, Chris D. 0000-0002-0485-7242 civey@usgs.gov","orcid":"https://orcid.org/0000-0002-0485-7242","contributorId":3308,"corporation":false,"usgs":true,"family":"Ivey","given":"Chris","email":"civey@usgs.gov","middleInitial":"D.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":821745,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Steevens, Jeffery A. 0000-0003-3946-1229","orcid":"https://orcid.org/0000-0003-3946-1229","contributorId":207511,"corporation":false,"usgs":true,"family":"Steevens","given":"Jeffery","middleInitial":"A.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":821746,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cleveland, Danielle M. 0000-0003-3880-4584 dcleveland@usgs.gov","orcid":"https://orcid.org/0000-0003-3880-4584","contributorId":187471,"corporation":false,"usgs":true,"family":"Cleveland","given":"Danielle","email":"dcleveland@usgs.gov","middleInitial":"M.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":821747,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Soucek, David J. 0000-0002-7741-0193","orcid":"https://orcid.org/0000-0002-7741-0193","contributorId":224591,"corporation":false,"usgs":false,"family":"Soucek","given":"David","email":"","middleInitial":"J.","affiliations":[{"id":40897,"text":"Illinois Natural History Survey, University of Illinois, Urbana-Champaign, IL","active":true,"usgs":false}],"preferred":false,"id":821748,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Dickinson, Amy","contributorId":224592,"corporation":false,"usgs":false,"family":"Dickinson","given":"Amy","email":"","affiliations":[{"id":40897,"text":"Illinois Natural History Survey, University of Illinois, Urbana-Champaign, IL","active":true,"usgs":false}],"preferred":false,"id":821749,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Van Genderen, Eric J.","contributorId":264611,"corporation":false,"usgs":false,"family":"Van Genderen","given":"Eric","email":"","middleInitial":"J.","affiliations":[{"id":54515,"text":"International Zinc Association, Durham NC","active":true,"usgs":false}],"preferred":false,"id":821750,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ryan, Adam C.","contributorId":175564,"corporation":false,"usgs":false,"family":"Ryan","given":"Adam","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":821751,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Schlekat, Chris E.","contributorId":264612,"corporation":false,"usgs":false,"family":"Schlekat","given":"Chris","email":"","middleInitial":"E.","affiliations":[{"id":54516,"text":"NiPERA Inc, Durham NC","active":true,"usgs":false}],"preferred":false,"id":821752,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Garman, Emily R.","contributorId":264613,"corporation":false,"usgs":false,"family":"Garman","given":"Emily R.","affiliations":[{"id":54516,"text":"NiPERA Inc, Durham NC","active":true,"usgs":false}],"preferred":false,"id":821753,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Middleton, Elizabeth 0000-0002-4775-2774","orcid":"https://orcid.org/0000-0002-4775-2774","contributorId":264614,"corporation":false,"usgs":false,"family":"Middleton","given":"Elizabeth","email":"","affiliations":[{"id":54516,"text":"NiPERA Inc, Durham NC","active":true,"usgs":false}],"preferred":false,"id":821754,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Santore, Robert C.","contributorId":202449,"corporation":false,"usgs":false,"family":"Santore","given":"Robert","email":"","middleInitial":"C.","affiliations":[{"id":36447,"text":"Windward Environmental LLC, Syracuse, NY","active":true,"usgs":false}],"preferred":false,"id":821755,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70221896,"text":"sir20215026 - 2021 - Hydrogeology of the Susquehanna River valley-fill aquifer system in the towns of Conklin and Kirkwood, Broome County, New York","interactions":[],"lastModifiedDate":"2024-06-26T19:36:08.52945","indexId":"sir20215026","displayToPublicDate":"2021-07-23T10:10:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-5026","displayTitle":"Hydrogeology of the Susquehanna River Valley-Fill Aquifer System in the Towns of Conklin and Kirkwood, Broome County, New York","title":"Hydrogeology of the Susquehanna River valley-fill aquifer system in the towns of Conklin and Kirkwood, Broome County, New York","docAbstract":"The hydrogeology of the Susquehanna River valley-fill aquifer system and adjacent areas in south-central Broome County, New York, was investigated in cooperation with the New York State Department of Environmental Conservation. The study area encompasses roughly 55.5 square miles and includes the towns of Conklin and Kirkwood. Multiple small, perhaps discontinuous, valley-fill aquifers of unknown extent and hydraulic interconnection underlie the Susquehanna River valley from easternmost Binghamton south to Riverside, New York, near the Pennsylvania border. The hydrogeologic framework of these aquifers is described in this report on the basis of existing descriptions of surficial materials, especially those related to deglaciation, and subsurface data extracted from well and boring logs. A compilation of surficial geology, the descriptions of the spatial distribution of confined and unconfined aquifers, hydrogeologic sections, and well locations is provided as an oversized map plate and in a U.S. Geological Survey data release.
Residential households are one of the principal consumers of groundwater in the study area. Approximately half of these households are served by public water-supply systems that obtain water from wells, chiefly from highly productive but small and likely discontinuous surficial deposits of sand and gravel, while others obtain water from sand-and-gravel aquifers beneath till and (or) fine-grained lacustrine deposits, and a few from bedrock. Residents outside the public-supply service areas rely on private wells. In till-mantled upland areas, nearly all private wells tap bedrock. Water-resource potential is likely greatest north of Kirkwood Center, New York, where the valley is narrowest, and local aquifers are in thick stratified glacial deposits. Well yields are highest in this part of the valley, and the local aquifer system is likely replenished through induced infiltration from the Susquehanna River and numerous small tributaries. The area between Langdon and Kirkwood is filled with a mixture of stratified and unstratified glacial sediments and contains one high-yield well. This area likely has moderate water-resource potential, but limited well data make this difficult to verify. Well yields from suitable stratified glacial sediments generally decrease southward toward Riverside, New York.
Characterizing potential groundwater resources is also helpful for prioritizing source-water-protection efforts. Water resources throughout New York are at risk of contamination from commercial and industrial surface activities. As in many valley areas throughout the Susquehanna River watershed in south-central New York, valley wells with depths greater than roughly 100 to 150 feet are susceptible to contamination by naturally occurring saltwater and methane. New York currently has a moratorium on hydraulic fracturing, but the study area is underlain by rocks suitable for unconventional methods of gas production that would likely be initiated if the moratorium were to be lifted.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215026","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Van Hoesen, J.G., Heisig, P.M., and Fisher, S.R., 2021, Hydrogeology of the Susquehanna River valley-fill aquifer system in the towns of Conklin and Kirkwood, Broome County, New York: U.S. Geological Survey Scientific Investigations Report 2021–5026, 29 p., 1 pl., https://doi.org/10.3133/sir20215026.","productDescription":"Report: vii, 29 p.; 1 Plate 30.25 x 31.25 inches; Data Release","numberOfPages":"29","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-118763","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":387155,"rank":4,"type":{"id":17,"text":"Plate"},"url":"https://pubs.usgs.gov/sir/2021/5026/sir20215026_plate1.pdf","text":"Plate 1","size":"1.82 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Detailed aquifer mapping of the Susquehanna River valley in south-central Broome County, towns of Conklin and Kirkwood, New York"},{"id":387154,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O1EAV7","text":"USGS data release","linkHelpText":"Digital datasets for the hydrogeology of the Susquehanna River Valley in south-central Broome County, towns of Conklin and Kirkwood, New York"},{"id":387153,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5026/sir20215026.pdf","text":"Report","size":"8.25 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5026"},{"id":387152,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5026/coverthb2.jpg"}],"country":"United States","state":"New York","county":"Broome County","otherGeospatial":"Susquehanna River Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.91690063476561,\n 42.001345689029755\n ],\n [\n -75.74970245361328,\n 42.001345689029755\n ],\n [\n -75.74970245361328,\n 42.08803181932636\n ],\n [\n -75.91690063476561,\n 42.08803181932636\n ],\n [\n -75.91690063476561,\n 42.001345689029755\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Forest management guidelines are designed to protect water quality from unintended effects of land use changes such as timber harvest, mining, or forest road construction. Although streams that periodically cease to flow (nonperennial) drain the majority of forested areas, these streams are not consistently included in forest management guidelines. This paper reviews management guidelines for nonperennial (intermittent and ephemeral) streams draining temperate forests in the continental U.S., evaluates potential impacts of land use activities on ecosystem services provided by these streams, and identifies information needed to incorporate nonperennial streams into water quality protection practices. For federally administered lands, national management guidance is deliberately nonprescriptive, deferring to regional and forest-level recommendations for both perennial and nonperennial streams. Most state guidelines recommend riparian management zone (RMZ) protection for perennial streams (48/50 states) and intermittent streams (45/50 states), but only Alaska and West Virginia require RMZs around ephemeral streams. Based on the National Hydrography Dataset, an average of 58% of forested land area in the U.S. drains to nonperennial headwater streams, making these stream types the most common connectors between forested lands and the aquatic system. Land uses that modify flow regimes in these streams can affect sediment and organic matter transport and distribution, stream temperature dynamics, and biogeochemical processing. Nonperennial streams also provide material subsidies to downstream waters and serve as temporary habitats for some aquatic species. However, limited research has examined how forest land uses affect ecosystem services and biota in these streams. Therefore we highlight a set of key questions about nonperennial streams in forests, not the least of which is simply understanding where headwater stream channels are located and associated patterns of flow duration. Although many questions remain, we also note where recent advances in data collection, modeling and process-level research provide opportunities to resolve uncertainties around nonperennial streams in forested landscapes of the continental U.S.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.foreco.2021.119523","usgsCitation":"Kampf, S.K., Dwyer, K., Fairchild, M.P., Dunham, J.B., Snyder, C.D., Jaeger, K.L., Luce, C., Hammond, J., Wilson, C., Zimmer, M., and Sidell, M., 2021, Managing nonperennial headwater streams in temperate forests of the United States: Forest Ecology and Management, v. 497, 119523, 16 p., https://doi.org/10.1016/j.foreco.2021.119523.","productDescription":"119523, 16 p.","ipdsId":"IP-128255","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":405997,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n -92.61,\n 48.45\n ],\n [\n -91.64,\n 48.14\n ],\n [\n -90.83,\n 48.27\n ],\n [\n -89.6,\n 48.01\n ],\n [\n -89.27292,\n 48.01981\n ],\n [\n -88.37811,\n 48.30292\n ],\n [\n -87.43979,\n 47.94\n ],\n [\n -86.46199,\n 47.55334\n ],\n [\n -85.65236,\n 47.22022\n ],\n [\n -84.87608,\n 46.90008\n ],\n [\n -84.77924,\n 46.6371\n ],\n [\n -84.54375,\n 46.53868\n ],\n [\n -84.6049,\n 46.4396\n ],\n [\n -84.3367,\n 46.40877\n ],\n [\n -84.14212,\n 46.51223\n ],\n [\n -84.09185,\n 46.27542\n ],\n [\n -83.89077,\n 46.11693\n ],\n [\n -83.61613,\n 46.11693\n ],\n [\n -83.46955,\n 45.99469\n ],\n [\n -83.59285,\n 45.81689\n ],\n [\n -82.55092,\n 45.34752\n ],\n [\n -82.33776,\n 44.44\n ],\n [\n -82.13764,\n 43.57109\n ],\n [\n -82.43,\n 42.98\n ],\n [\n -82.9,\n 42.43\n ],\n [\n -83.12,\n 42.08\n ],\n [\n -83.142,\n 41.97568\n ],\n [\n -83.02981,\n 41.8328\n ],\n [\n -82.69009,\n 41.67511\n ],\n [\n -82.43928,\n 41.67511\n ],\n [\n -81.27775,\n 42.20903\n ],\n [\n -80.24745,\n 42.3662\n ],\n [\n -78.93936,\n 42.86361\n ],\n [\n -78.92,\n 42.965\n ],\n [\n -79.01,\n 43.27\n ],\n [\n -79.17167,\n 43.46634\n ],\n [\n -78.72028,\n 43.62509\n ],\n [\n -77.73789,\n 43.62906\n ],\n [\n -76.82003,\n 43.62878\n ],\n [\n -76.5,\n 44.01846\n ],\n [\n -76.375,\n 44.09631\n ],\n [\n -75.31821,\n 44.81645\n ],\n [\n -74.867,\n 45.00048\n ],\n [\n -73.34783,\n 45.00738\n ],\n [\n -71.50506,\n 45.0082\n ],\n [\n -71.405,\n 45.255\n ],\n [\n -71.08482,\n 45.30524\n ],\n [\n -70.66,\n 45.46\n ],\n [\n -70.305,\n 45.915\n ],\n [\n -69.99997,\n 46.69307\n ],\n [\n -69.23722,\n 47.44778\n ],\n [\n -68.905,\n 47.185\n ],\n [\n -68.23444,\n 47.35486\n ],\n [\n -67.79046,\n 47.06636\n ],\n [\n -67.79134,\n 45.70281\n ],\n [\n -67.13741,\n 45.13753\n ],\n [\n -66.96466,\n 44.8097\n ],\n [\n -68.03252,\n 44.3252\n ],\n [\n -69.06,\n 43.98\n ],\n [\n -70.11617,\n 43.68405\n ],\n [\n -70.64548,\n 43.09024\n ],\n [\n -70.81489,\n 42.8653\n ],\n [\n -70.825,\n 42.335\n ],\n [\n -70.495,\n 41.805\n ],\n [\n -70.08,\n 41.78\n ],\n [\n -70.185,\n 42.145\n ],\n [\n -69.88497,\n 41.92283\n ],\n [\n -69.96503,\n 41.63717\n ],\n [\n -70.64,\n 41.475\n ],\n [\n -71.12039,\n 41.49445\n ],\n [\n -71.86,\n 41.32\n ],\n [\n -72.295,\n 41.27\n ],\n [\n -72.87643,\n 41.22065\n ],\n [\n -73.71,\n 40.9311\n ],\n [\n -72.24126,\n 41.11948\n ],\n [\n -71.945,\n 40.93\n ],\n [\n -73.345,\n 40.63\n ],\n [\n -73.982,\n 40.628\n ],\n [\n -73.95232,\n 40.75075\n ],\n [\n -74.25671,\n 40.47351\n ],\n [\n -73.96244,\n 40.42763\n ],\n [\n -74.17838,\n 39.70926\n ],\n [\n -74.90604,\n 38.93954\n ],\n [\n -74.98041,\n 39.1964\n ],\n [\n -75.20002,\n 39.24845\n ],\n [\n -75.52805,\n 39.4985\n ],\n [\n -75.32,\n 38.96\n ],\n [\n -75.07183,\n 38.78203\n ],\n [\n -75.05673,\n 38.40412\n ],\n [\n -75.37747,\n 38.01551\n ],\n [\n -75.94023,\n 37.21689\n ],\n [\n -76.03127,\n 37.2566\n ],\n [\n -75.72205,\n 37.93705\n ],\n [\n -76.23287,\n 38.31921\n ],\n [\n -76.35,\n 39.15\n ],\n [\n -76.54272,\n 38.71762\n ],\n [\n -76.32933,\n 38.08326\n ],\n [\n -76.99,\n 38.23999\n ],\n [\n -76.30162,\n 37.91794\n ],\n [\n -76.25874,\n 36.9664\n ],\n [\n -75.9718,\n 36.89726\n ],\n [\n -75.86804,\n 36.55125\n ],\n [\n -75.72749,\n 35.55074\n ],\n [\n -76.36318,\n 34.80854\n ],\n [\n -77.39763,\n 34.51201\n ],\n [\n -78.05496,\n 33.92547\n ],\n [\n -78.55435,\n 33.86133\n ],\n [\n -79.06067,\n 33.49395\n ],\n [\n -79.20357,\n 33.15839\n ],\n [\n -80.30132,\n 32.50935\n ],\n [\n -80.86498,\n 32.0333\n ],\n [\n -81.33629,\n 31.44049\n ],\n [\n -81.49042,\n 30.72999\n ],\n [\n -81.31371,\n 30.03552\n ],\n [\n -80.98,\n 29.18\n ],\n [\n -80.53558,\n 28.47213\n ],\n [\n -80.53,\n 28.04\n ],\n [\n -80.05654,\n 26.88\n ],\n [\n -80.08801,\n 26.20576\n ],\n [\n -80.13156,\n 25.81677\n ],\n [\n -80.38103,\n 25.20616\n ],\n [\n -80.68,\n 25.08\n ],\n [\n -81.17213,\n 25.20126\n ],\n [\n -81.33,\n 25.64\n ],\n [\n -81.71,\n 25.87\n ],\n [\n -82.24,\n 26.73\n ],\n [\n -82.70515,\n 27.49504\n ],\n [\n -82.85526,\n 27.88624\n ],\n [\n -82.65,\n 28.55\n ],\n [\n -82.93,\n 29.1\n ],\n [\n -83.70959,\n 29.93656\n ],\n [\n -84.1,\n 30.09\n ],\n [\n -85.10882,\n 29.63615\n ],\n [\n -85.28784,\n 29.68612\n ],\n [\n -85.7731,\n 30.15261\n ],\n [\n -86.4,\n 30.4\n ],\n [\n -87.53036,\n 30.27433\n ],\n [\n -88.41782,\n 30.3849\n ],\n [\n -89.18049,\n 30.31598\n ],\n [\n -89.59383,\n 30.15999\n ],\n [\n -89.41373,\n 29.89419\n ],\n [\n -89.43,\n 29.48864\n ],\n [\n -89.21767,\n 29.29108\n ],\n [\n -89.40823,\n 29.15961\n ],\n [\n -89.77928,\n 29.30714\n ],\n [\n -90.15463,\n 29.11743\n ],\n [\n -90.88022,\n 29.14854\n ],\n [\n -91.62678,\n 29.677\n ],\n [\n -92.49906,\n 29.5523\n ],\n [\n -93.22637,\n 29.78375\n ],\n [\n -93.84842,\n 29.71363\n ],\n [\n -94.69,\n 29.48\n ],\n [\n -95.60026,\n 28.73863\n ],\n [\n -96.59404,\n 28.30748\n ],\n [\n -97.14,\n 27.83\n ],\n [\n -97.37,\n 27.38\n ],\n [\n -97.38,\n 26.69\n ],\n [\n -97.33,\n 26.21\n ],\n [\n -97.14,\n 25.87\n ],\n [\n -97.53,\n 25.84\n ],\n [\n -98.24,\n 26.06\n ],\n [\n -99.02,\n 26.37\n ],\n [\n -99.3,\n 26.84\n ],\n [\n -99.52,\n 27.54\n ],\n [\n -100.11,\n 28.11\n ],\n [\n -100.45584,\n 28.69612\n ],\n [\n -100.9576,\n 29.38071\n ],\n [\n -101.6624,\n 29.7793\n ],\n [\n -102.48,\n 29.76\n ],\n [\n -103.11,\n 28.97\n ],\n [\n -103.94,\n 29.27\n ],\n [\n -104.45697,\n 29.57196\n ],\n [\n -104.70575,\n 30.12173\n ],\n [\n -105.03737,\n 30.64402\n ],\n [\n -105.63159,\n 31.08383\n ],\n [\n -106.1429,\n 31.39995\n ],\n [\n -106.50759,\n 31.75452\n ],\n [\n -108.24,\n 31.75485\n ],\n [\n -108.24194,\n 31.34222\n ],\n [\n -109.035,\n 31.34194\n ],\n [\n -111.02361,\n 31.33472\n ],\n [\n -113.30498,\n 32.03914\n ],\n [\n -114.815,\n 32.52528\n ],\n [\n -114.72139,\n 32.72083\n ],\n [\n -115.99135,\n 32.61239\n ],\n [\n -117.12776,\n 32.53534\n ],\n [\n -117.29594,\n 33.04622\n ],\n [\n -117.944,\n 33.62124\n ],\n [\n -118.4106,\n 33.74091\n ],\n [\n -118.51989,\n 34.02778\n ],\n [\n -119.081,\n 34.078\n ],\n [\n -119.43884,\n 34.34848\n ],\n [\n -120.36778,\n 34.44711\n ],\n [\n -120.62286,\n 34.60855\n ],\n [\n -120.74433,\n 35.15686\n ],\n [\n -121.71457,\n 36.16153\n ],\n [\n -122.54747,\n 37.55176\n ],\n [\n -122.51201,\n 37.78339\n ],\n [\n -122.95319,\n 38.11371\n ],\n [\n -123.7272,\n 38.95166\n ],\n [\n -123.86517,\n 39.76699\n ],\n [\n -124.39807,\n 40.3132\n ],\n [\n -124.17886,\n 41.14202\n ],\n [\n -124.2137,\n 41.99964\n ],\n [\n -124.53284,\n 42.76599\n ],\n [\n -124.14214,\n 43.70838\n ],\n [\n -124.02053,\n 44.6159\n ],\n [\n -123.89893,\n 45.52341\n ],\n [\n -124.07963,\n 46.86475\n ],\n [\n -124.39567,\n 47.72017\n ],\n [\n -124.68721,\n 48.18443\n ],\n [\n -124.5661,\n 48.37971\n ],\n [\n -123.12,\n 48.04\n ],\n [\n -122.58736,\n 47.096\n ],\n [\n -122.34,\n 47.36\n ],\n [\n -122.5,\n 48.18\n ],\n [\n -122.84,\n 49\n ],\n [\n -120,\n 49\n ],\n [\n -117.03121,\n 49\n ],\n [\n -116.04818,\n 49\n ],\n [\n -113,\n 49\n ],\n [\n -110.05,\n 49\n ],\n [\n -107.05,\n 49\n ],\n [\n -104.04826,\n 48.99986\n ],\n [\n -100.65,\n 49\n ],\n [\n -97.22872,\n 49.0007\n ],\n [\n -95.15907,\n 49\n ],\n [\n -95.15609,\n 49.38425\n ],\n [\n -94.81758,\n 49.38905\n ]\n ]\n ]\n ]\n },\n \"properties\": {\n \"name\": \"United States\"\n }\n }\n ]\n}","volume":"497","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kampf, Stephanie K. 0000-0001-8991-2679","orcid":"https://orcid.org/0000-0001-8991-2679","contributorId":225146,"corporation":false,"usgs":false,"family":"Kampf","given":"Stephanie","email":"","middleInitial":"K.","affiliations":[{"id":41048,"text":"Associate Professor, Department of Ecosystem Science and Sustainability, Colorado State University","active":true,"usgs":false}],"preferred":false,"id":850388,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dwyer, Kathleen","contributorId":296016,"corporation":false,"usgs":false,"family":"Dwyer","given":"Kathleen","email":"","affiliations":[{"id":16848,"text":"USDA Forest Service, Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":850389,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fairchild, Matthew P.","contributorId":196533,"corporation":false,"usgs":false,"family":"Fairchild","given":"Matthew","email":"","middleInitial":"P.","affiliations":[{"id":24595,"text":"USDA Forest Service, Fort Collins CO","active":true,"usgs":false}],"preferred":false,"id":850390,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dunham, Jason B. 0000-0002-6268-0633 jdunham@usgs.gov","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":147808,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","email":"jdunham@usgs.gov","middleInitial":"B.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":850391,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Snyder, Craig D. 0000-0002-3448-597X csnyder@usgs.gov","orcid":"https://orcid.org/0000-0002-3448-597X","contributorId":2568,"corporation":false,"usgs":true,"family":"Snyder","given":"Craig","email":"csnyder@usgs.gov","middleInitial":"D.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":850392,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jaeger, Kristin L. 0000-0002-1209-8506","orcid":"https://orcid.org/0000-0002-1209-8506","contributorId":206935,"corporation":false,"usgs":true,"family":"Jaeger","given":"Kristin","middleInitial":"L.","affiliations":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"preferred":true,"id":850393,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Luce, Charles H.","contributorId":245593,"corporation":false,"usgs":false,"family":"Luce","given":"Charles H.","affiliations":[{"id":40027,"text":"United States Forest Service","active":true,"usgs":false}],"preferred":false,"id":850394,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hammond, John C. 0000-0002-4935-0736","orcid":"https://orcid.org/0000-0002-4935-0736","contributorId":223108,"corporation":false,"usgs":true,"family":"Hammond","given":"John C.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":850395,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wilson, Codie","contributorId":296021,"corporation":false,"usgs":false,"family":"Wilson","given":"Codie","email":"","affiliations":[{"id":63967,"text":"Natural Resource Ecology Lab, Colorado State University","active":true,"usgs":false}],"preferred":false,"id":850396,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Zimmer, Margaret","contributorId":296022,"corporation":false,"usgs":false,"family":"Zimmer","given":"Margaret","affiliations":[{"id":27155,"text":"University of California Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":850397,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Sidell, Marielle","contributorId":296023,"corporation":false,"usgs":false,"family":"Sidell","given":"Marielle","email":"","affiliations":[{"id":63968,"text":"Department of Ecosystem Science and Sustainability, Colorado State University","active":true,"usgs":false}],"preferred":false,"id":850398,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70222612,"text":"70222612 - 2021 - Applying biodiversity metrics as surrogates to a habitat conservation plan","interactions":[],"lastModifiedDate":"2021-08-09T13:40:23.125976","indexId":"70222612","displayToPublicDate":"2021-07-23T08:27:41","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5021,"text":"Environments","active":true,"publicationSubtype":{"id":10}},"title":"Applying biodiversity metrics as surrogates to a habitat conservation plan","docAbstract":"Unabated urbanization has led to environmental degradation and subsequent biodiversity loss across the globe. As an outcome of unmitigated land use, multi-jurisdictional agencies have developed land use plans that attempt to protect threatened or endangered species across selected areas by which some trade-offs between harm to species and additional conservation approaches are allowed among the partnering organizations. Typical conservation plans can be created to focus on single or multiple species, and although they may protect a species or groups of species, they may not account for biodiversity or its protection across the given area. We applied an approach that clustered deductive habitat models for terrestrial vertebrates into metrics that serve as surrogates for biodiversity and relate to ecosystem services. In order to evaluate this process, we collaborated with the partnering agencies who are creating a Multi-Species Habitat Conservation Plan in southern California and compared it to the entire Mojave Desert Ecoregion. We focused on total terrestrial vertebrate species richness and taxon groupings representing amphibians, birds, mammals, and reptiles, and two special status species using the Normalized Index of Biodiversity (NIB). The conservation planning area had a lower NIB and was less species rich than the Mojave Desert Ecoregion, but the Mojave River riparian corridor had a higher NIB and was more species-rich, and while taxon analysis varied across the geographies, this pattern generally held. Additionally, we analyzed desert tortoise (Gopherus agassizii) and desert kit fox (Vulpes macrotis arsipus) as umbrella species and determined that both species are associated with increased NIB and large numbers of species for the conservation area. Our process provided the ability to incorporate value-added surrogate information into a formal land use planning process and used a metric, NIB, which allowed comparison of the various planning areas and geographic units. Although this process has been applied to Apple Valley, CA, and other geographies within the U.S., the approach has practical application for other global biodiversity initiatives.
","language":"English","publisher":"MDPI","doi":"10.3390/environments8080069","usgsCitation":"Boykin, K.G., Kepner, W.G., and McKerrow, A., 2021, Applying biodiversity metrics as surrogates to a habitat conservation plan: Environments, v. 8, no. 8, 69, 19 p., https://doi.org/10.3390/environments8080069.","productDescription":"69, 19 p.","ipdsId":"IP-128000","costCenters":[{"id":38128,"text":"Science Analytics and Synthesis","active":true,"usgs":true}],"links":[{"id":451420,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/environments8080069","text":"Publisher Index Page"},{"id":387777,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"San Bernardino County","otherGeospatial":"Apple Valley Multi-Species Habitat Conservation Plan Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.28666667,\n 34.73750000\n ],\n [\n -116.95666667,\n 34.73750000\n ],\n [\n -116.95666667,\n 34.37194444\n ],\n [\n -117.28666667,\n 34.37194444\n ],\n [\n -117.28666667,\n 34.73750000\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"8","noUsgsAuthors":false,"publicationDate":"2021-07-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Boykin, Kenneth G. 0000-0001-6381-0463","orcid":"https://orcid.org/0000-0001-6381-0463","contributorId":43651,"corporation":false,"usgs":false,"family":"Boykin","given":"Kenneth","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":820747,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kepner, William G.","contributorId":261909,"corporation":false,"usgs":false,"family":"Kepner","given":"William","email":"","middleInitial":"G.","affiliations":[{"id":13226,"text":"U.S. Environmental Protection Agency, Office of Research and Development","active":true,"usgs":false}],"preferred":false,"id":820748,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McKerrow, Alexa 0000-0002-8312-2905 amckerrow@usgs.gov","orcid":"https://orcid.org/0000-0002-8312-2905","contributorId":127753,"corporation":false,"usgs":true,"family":"McKerrow","given":"Alexa","email":"amckerrow@usgs.gov","affiliations":[{"id":208,"text":"Core Science Analytics and Synthesis","active":true,"usgs":true}],"preferred":true,"id":820749,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70223505,"text":"70223505 - 2021 - Interlaboratory comparison of SARS-CoV2 molecular detection assays in use by U.S. veterinary diagnostic laboratories","interactions":[],"lastModifiedDate":"2021-11-01T15:59:00.301607","indexId":"70223505","displayToPublicDate":"2021-07-23T08:21:17","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2492,"text":"Journal of Veterinary Diagnostic Investigation","active":true,"publicationSubtype":{"id":10}},"title":"Interlaboratory comparison of SARS-CoV2 molecular detection assays in use by U.S. veterinary diagnostic laboratories","docAbstract":"The continued search for intermediate hosts and potential reservoirs for SARS-CoV2 makes it clear that animal surveillance is critical in outbreak response and prevention. Real-time RT-PCR assays for SARS-CoV2 detection can easily be adapted to different host species. U.S. veterinary diagnostic laboratories have used the CDC assays or other national reference laboratory methods to test animal samples. However, these methods have only been evaluated using internal validation protocols. To help the laboratories evaluate their SARS-CoV2 test methods, an interlaboratory comparison (ILC) was performed in collaboration with multiple organizations. Forty-four sets of 19 blind-coded RNA samples in Tris-EDTA (TE) buffer or PrimeStore transport medium were shipped to 42 laboratories. Results were analyzed according to the principles of the International Organization for Standardization (ISO) 16140-2:2016 standard. Qualitative assessment of PrimeStore samples revealed that, in approximately two-thirds of the laboratories, the limit of detection with a probability of 0.95 (LOD95) for detecting the RNA was ≤20 copies per PCR reaction, close to the theoretical LOD of 3 copies per reaction. This level of sensitivity is not expected in clinical samples because of additional factors, such as sample collection, transport, and extraction of RNA from the clinical matrix. Quantitative assessment of Ct values indicated that reproducibility standard deviations for testing the RNA with assays reported as N1 were slightly lower than those for N2, and they were higher for the RNA in PrimeStore medium than those in TE buffer. Analyst experience and the use of either a singleplex or multiplex PCR also affected the quantitative ILC test results.
In recent decades, feral horse (Equus caballus; horse) populations increased in sagebrush (Artimesia spp.) ecosystems, especially within the Great Basin, to the point of exceeding maximum appropriate management levels (AMLmax), which were set by land administrators to balance resource use by feral horses, livestock, and wildlife. Concomitantly, greater sage-grouse (Centrocercus urophasianus; sage-grouse) are sagebrush obligates that have experienced population declines within these same arid environments as a result of steady and continued loss of seasonal habitats. Although a strong body of research indicates that overabundant populations of horses degrade sagebrush ecosystems, empirical evidence linking horse abundance to sage-grouse population dynamics is missing. Within a Bayesian framework, we employed state-space models to estimate population rate of change (λ) using 15 years (2005–2019) of count surveys of male sage-grouse at traditional breeding grounds (i.e., leks) as a function of horse abundance relative to AMLmax and other environmental covariates (e.g., wildfire, precipitation, % sagebrush cover). Additionally, we employed a post hoc impact-control design to validate existing AMLmax values as related to sage-grouse population responses, and to help control for environmental stochasticity and broad-scale oscillations in sage-grouse abundance. On average, for every 50% increase in horse abundance over AMLmax, our model predicted an annual decline in sage-grouse abundance by 2.6%. Horse abundance at or below AMLmax coincided with sage-grouse λ estimates that were consistent with trends at non-horse areas elsewhere in the study region. Thus, AMLmax, as a whole, appeared to be set adequately in preventing adverse effects to sage-grouse populations. Results indicated 76%, 97%, and >99% probability of sage-grouse population decline relative to controls when horse numbers are 2, 2.5, and ≥3 times over AMLmax, respectively. As of 2019, horse herds exceeded AMLmax in Nevada, USA, by >4 times on average across all horse management areas. If feral horse populations continue to grow at current rates unabated, model projections indicate sage-grouse populations will be reduced within horse-occupied areas by >70.0% by 2034 (15-year projection), on average compared to 21.2% estimated for control sites. A monitoring framework that improves on estimating horse abundance and identifying responses of sage-grouse and other key indicator species (plant and animal) would be beneficial to guide management decisions that promote co-occurrence of horses with sensitive wildlife and livestock within landscapes subjected to multiple uses. Published 2021. This article is a U.S. Government work and is in the public domain in the USA. The Journal of Wildlife Management published by Wiley Periodicals LLC on behalf of The Wildlife Society.
Wildfires and invasive species have caused widespread changes in western North America’s shrub-steppe landscapes. The bottom–up consequences of degraded shrublands on predator ecology and demography remain poorly understood. We used a before–after paired design to study whether Golden Eagle (Aquila chrysaetos) diet and nestling survivorship changed following wildfires in southwestern Idaho, USA. We assessed burn extents from 1981 to 2013 and vegetation changes between 1979 (pre-burn) and 2014 (post-burn) within 3 km of Golden Eagle nesting centroids. We measured the frequency and biomass of individual prey, calculated diet diversity indexes, and monitored nestling survivorship at 15 territories in 1971–1981 and 2014–2015. On average, 0.70 of the area within 3 km of nesting centroids burned between 1981 and 2013, and the mean proportion of unburned shrubland decreased from 0.73 in 1979 to 0.22 in 2014. Diets in post-burn years were more diverse and had a lower proportion of some shrub-associated species, such as black-tailed jackrabbits (Lepus californicus) and mountain cottontails (Sylvilagus nuttallii), and a higher proportion of American Coots (Fulica americana), Mallards (Anas platyrhynchos), Piute ground squirrels (Urocitellus mollis), and Rock Pigeons (Columba livia) compared with pre-burn years. A high proportion of waterfowl represented a novel change in Golden Eagle diets, which are typically dominated by mammalian prey. Nestling survivorship was positively associated with the proportion of black-tailed jackrabbits and negatively associated with the proportion of Rock Pigeons in eagle diets. Rock Pigeons are a vector for Trichomonas gallinae, a disease-causing protozoan lethal to young eagles. Nesting attempts were more likely to fail (all young die) in the post-burn period compared with the pre-burn period. Dietary shifts are a common mechanism for predators to cope with landscape change, but shifts away from preferred prey to disease vectors affect nestling survivorship and could lead to population-level effects on productivity.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/ornithapp/duab034","usgsCitation":"Heath, J.A., Kochert, M.N., and Steenhof, K., 2021, Golden Eagle dietary shifts following wildfire and shrub loss have negative consequences for nestling survivorship: Ornithological Applications, v. 123, no. 4, duab034, 14 p., https://doi.org/10.1093/ornithapp/duab034.","productDescription":"duab034, 14 p.","ipdsId":"IP-126722","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":451429,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/ornithapp/duab034","text":"Publisher Index Page"},{"id":401967,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.00439453125,\n 42.61779143282346\n ],\n [\n -114.9169921875,\n 42.61779143282346\n ],\n [\n -114.9169921875,\n 43.77109381775651\n ],\n [\n -117.00439453125,\n 43.77109381775651\n ],\n [\n -117.00439453125,\n 42.61779143282346\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"123","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-07-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Heath, Julie A.","contributorId":192842,"corporation":false,"usgs":false,"family":"Heath","given":"Julie","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":844412,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kochert, Michael N. 0000-0002-4380-3298 mkochert@usgs.gov","orcid":"https://orcid.org/0000-0002-4380-3298","contributorId":3037,"corporation":false,"usgs":true,"family":"Kochert","given":"Michael","email":"mkochert@usgs.gov","middleInitial":"N.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":844413,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Steenhof, Karen karen_steenhof@usgs.gov","contributorId":203439,"corporation":false,"usgs":false,"family":"Steenhof","given":"Karen","email":"karen_steenhof@usgs.gov","affiliations":[],"preferred":false,"id":844414,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70249293,"text":"70249293 - 2021 - The products of primary magma fragmentation finally revealed by pumice agglomerates","interactions":[],"lastModifiedDate":"2023-10-03T12:11:05.896246","indexId":"70249293","displayToPublicDate":"2021-07-23T07:07:31","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1796,"text":"Geology","active":true,"publicationSubtype":{"id":10}},"title":"The products of primary magma fragmentation finally revealed by pumice agglomerates","docAbstract":"Following rapid decompression in the conduit of a volcano, magma breaks into ash- to block-sized fragments, powering explosive sub-Plinian and Plinian eruptions that may generate destructive pyroclastic falls and flows. It is thus crucial to assess how magma breaks up into fragments. This task is difficult, however, because of the subterranean nature of the entire process and because the original size of pristine fragments is modified by secondary fragmentation and expansion. New textural observations of sub-Plinian and Plinian pumice lapilli reveal that some primary products of magma fragmentation survive by sintering together within seconds of magma break-up. Their size distributions reflect the energetics of fragmentation, consistent with products of rapid decompression experiments. Pumice aggregates thus offer a unique window into the previously inaccessible primary fragmentation process and could be used to determine the potential energy of fragmentation.
This response is offered to the critique by Gard et al. (2021) of our meta-analysis of polychlorinated biphenyl (PCB)-induced toxicity data in fish (Berninger and Tillitt 2019). Gard et al. (2021) offered numerous comments, the most substantive suggesting that 1) we should have added no-observable–adverse effect residue (NOAER) data from additional studies and all data points from selected studies, and 2) the uncertainty of aggregating data from different PCB mixtures, different species, and different life stages is too great based on a limited data set. The additional studies Gard et al. suggested either were not designed to produce toxicological data, had experimental design issues, were confounded by co-contaminants, or did not contain paired exposure–effects data and as such were not appropriate to add to the data set. Lowest-observable–adverse effect residue (LOAER) values were selected for our analysis because they represent population sensitivities from the central portions of a frequency distribution (the linear portion of dose–response curves). As a consequence, there is less uncertainty in these input data (LOAER values) and greater confidence that they accurately represent the response of fish populations tested. Modeling NOAER values is in the extrapolation portion of a dose–response relationship and subject to enhanced uncertainty. The Gard et al. (2021) critique ignores this fundamental principle of toxicology and adds/deletes data points from our data set without clear selection criteria, which artificially enhances the uncertainty of their models that ultimately are not useful. We reject the premise that it is better to use individual study data as opposed to aggregation of PCB-induced toxicity thresholds in fish.
","language":"English","publisher":"Society for Environmental Toxicology and Chemistry (SETAC)","doi":"10.1002/etc.5074","usgsCitation":"Berninger, J., and Tillitt, D.E., 2021, Response to Gard et al.'s (2021) Comments on the Critical Review “Polychlorinated Biphenyl Tissue-Concentration Thresholds for Survival, Growth, and Reproduction in Fish”: Environmental Toxicology and Chemistry, v. 8, no. 40, p. 2098-2109, https://doi.org/10.1002/etc.5074.","productDescription":"12 p.","startPage":"2098","endPage":"2109","ipdsId":"IP-127386","costCenters":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"links":[{"id":498904,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/etc.5074","text":"Publisher Index Page"},{"id":387596,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"40","noUsgsAuthors":false,"publicationDate":"2021-08-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Berninger, Jason P.","contributorId":173602,"corporation":false,"usgs":false,"family":"Berninger","given":"Jason P.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":820064,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tillitt, Donald E. 0000-0002-8278-3955 dtillitt@usgs.gov","orcid":"https://orcid.org/0000-0002-8278-3955","contributorId":1875,"corporation":false,"usgs":true,"family":"Tillitt","given":"Donald","email":"dtillitt@usgs.gov","middleInitial":"E.","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":820065,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70222488,"text":"70222488 - 2021 - Drivers of seedling establishment success in dryland restoration efforts","interactions":[],"lastModifiedDate":"2023-07-20T14:03:49.530468","indexId":"70222488","displayToPublicDate":"2021-07-22T08:23:43","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6505,"text":"Nature Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Drivers of seedling establishment success in dryland restoration efforts","docAbstract":"Restoration of degraded drylands is urgently needed to mitigate climate change, reverse desertification and secure livelihoods for the two billion people who live in these areas. Bold global targets have been set for dryland restoration to restore millions of hectares of degraded land. These targets have been questioned as overly ambitious, but without a global evaluation of successes and failures it is impossible to gauge feasibility. Here we examine restoration seeding outcomes across 174 sites on six continents, encompassing 594,065 observations of 671 plant species. Our findings suggest reasons for optimism. Seeding had a positive impact on species presence: in almost a third of all treatments, 100% of species seeded were growing at first monitoring. However, dryland restoration is risky: 17% of projects failed, with no establishment of any seeded species, and consistent declines were found in seeded species as projects matured. Across projects, higher seeding rates and larger seed sizes resulted in a greater probability of recruitment, with further influences on species success including site aridity, taxonomic identity and species life form. Our findings suggest that investigations examining these predictive factors will yield more effective and informed restoration decision-making.
Hatchery-raised, age-0 Florida bass Micropterus floridanus are commonly used for fish enhancement efforts to support popular recreational fisheries and are ecologically important as both a food source and consumer. Despite their importance and frequent use of submerged aquatic vegetation (SAV) habitats, critical information is lacking on the specific characteristics of SAV that influence habitat occupancy. Using the SAV species Vallisneria americana and Potamogeton illinoensis, which are native to the southeast USA, and the invasive SAV Hydrilla verticillata, we conducted seven different habitat choice experiments to examine hatchery-raised, age-0 M. floridanus habitat use of different SAV populations (i.e., hydrologically isolated collection sources of varied physical characteristics), population diversity (i.e., increased richness of genotypically and phenotypically variable SAV), species, and species diversity (i.e., increased species richness). Fish spent more time in taller, larger V. americana but did not seem to favor any particular P. illinoensis population, SAV species, or species diversity tested. Additionally, fish spent more time in increased V. americana population diversity when the populations used were randomized, but fish spent more time in decreased population diversity when their favored V. americana population was used in all choices. This research adds additional nuance to our understanding of optimal vegetation for fish habitat use and is informative for future SAV plantings and invasive SAV management aimed at maximizing fish habitat and restoring recreational fisheries.
In the Northern Rockies of the United States, predators like wolves (Canis lupus) and mountain lions (Puma concolor) have been implicated in fluctuations or declines in populations of game species like elk (Cervus canadensis) and mule deer (Odocoileus hemionus). In particular, local distributions of these predators may affect ungulate behavior, use of space, and dynamics. Our goal was to develop generalizable predictions of habitat selection by wolves and mountain lions across western Montana. We hypothesized both predator species would select habitat that maximized their chances of encountering and killing ungulates and that minimized their chances of encountering humans. We assessed habitat selection by these predators during summer using within-home range (3rd order) resource selection functions (RSFs) in multiple study areas throughout western Montana, and tested how generalizable RSF predictions were by applying them to out-of-sample telemetry data from separate study areas. Selection for vegetation cover-types varied substantially among wolves in different study areas. Nonetheless, our predictions of 3rd order selection by wolves were highly generalizable across different study areas. Wolves consistently selected simple topography where ungulate prey may be more susceptible to their cursorial hunting mode. Topographic features may serve as better proxies of predation risk by wolves than vegetation cover-types. Predictions of mountain lion distribution were less generalizable. Use of rugged terrain by mountain lions varied across ecosystem-types, likely because mountain lions targeted the habitats of different prey species in each study area. Our findings suggest that features that facilitate the hunting mode of a predator (i.e. simple topography for cursorial predators and hiding cover for stalking predators) may be more generalizable predictors of their habitat selection than features associated with local prey densities.