The geomorphological formation of the Baja California peninsula and the Gulf of California is a principal driver of diversification for the reptiles of North America’s warm deserts. The western banded gecko, Coleonyx variegatus, is distributed throughout the Mojave, Sonoran and Peninsular deserts. In this study we use multilocus sequence data to address deep phylogeographic structure within C. variegatus. Analyses of mtDNA data recover six divergent clades throughout the range of C. variegatus. Topology of the mtDNA gene tree suggests separate origins of peninsular populations with an older lineage in the south and a younger one in the north. In contrast, analyses of multilocus nuclear data provide support for four lineages, corresponding to the subspecies C. v. abbotti, C. v. peninsularis, C. v. sonoriensis and C. v. variegatus. Phylogenetic analyses of the nuclear data recover C. v. abbotti and C. v. peninsularis as a clade, indicating a single origin of the peninsular populations. Discordance between the nuclear and mtDNA data is largely the result of repeated episodes of mtDNA introgression that have obscured both lineage boundaries and biogeographic history. Dating analyses of the combined nuclear and mtDNA data suggest that the peninsular clade diverged from the continental group in the Late Miocene.
","language":"English","publisher":"Oxford University Press on behalf of The Linnean Society of London","doi":"10.1093/zoolinnean/zlz143","usgsCitation":"Leavitt, D.H., Hollingsworth, B., Fisher, R.N., and Reeder, T.W., 2020, Introgression obscures lineage boundaries and phylogeographic history in the western banded gecko, Coleonyx variegatus (Squamata: Eublepharidae): Zoological Journal of the Linnean Society, v. 190, no. 13, p. 181-226, https://doi.org/10.1093/zoolinnean/zlz143.","productDescription":"46 p.","startPage":"181","endPage":"226","ipdsId":"IP-113109","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":371663,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico","otherGeospatial":"Baja California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.333984375,\n 32.99023555965106\n ],\n [\n -116.54296874999999,\n 28.459033019728043\n ],\n [\n -113.99414062499999,\n 22.59372606392931\n ],\n [\n -106.962890625,\n 20.2209657795223\n ],\n [\n -105.908203125,\n 22.67484735118852\n ],\n [\n -110.302734375,\n 27.371767300523047\n ],\n [\n -114.521484375,\n 32.47269502206151\n ],\n [\n -117.333984375,\n 32.99023555965106\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"190","issue":"13","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2020-01-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Leavitt, Dean H","contributorId":221884,"corporation":false,"usgs":false,"family":"Leavitt","given":"Dean","email":"","middleInitial":"H","affiliations":[{"id":6608,"text":"San Diego State University","active":true,"usgs":false}],"preferred":false,"id":780624,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hollingsworth, Bradford","contributorId":202768,"corporation":false,"usgs":false,"family":"Hollingsworth","given":"Bradford","affiliations":[{"id":36525,"text":"San Diego Museum of Natural History","active":true,"usgs":false}],"preferred":false,"id":780626,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fisher, Robert N. 0000-0002-2956-3240 rfisher@usgs.gov","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":1529,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert","email":"rfisher@usgs.gov","middleInitial":"N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":780623,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reeder, Tod W","contributorId":221885,"corporation":false,"usgs":false,"family":"Reeder","given":"Tod","email":"","middleInitial":"W","affiliations":[{"id":6608,"text":"San Diego State University","active":true,"usgs":false}],"preferred":false,"id":780625,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70211225,"text":"70211225 - 2020 - Estimating detection probability for Burmese Pythons with few detections and zero recapture events","interactions":[],"lastModifiedDate":"2020-07-21T14:32:45.799268","indexId":"70211225","displayToPublicDate":"2020-01-20T14:57:18","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2334,"text":"Journal of Herpetology","active":true,"publicationSubtype":{"id":10}},"title":"Estimating detection probability for Burmese Pythons with few detections and zero recapture events","docAbstract":"Detection has been a long-standing challenge to monitoring populations of cryptic herpetofauna, which often have detection probabilities that are closer to zero than one. Burmese Pythons (Python bivittatus =Python molurus bivittatus), a recent invader in the Greater Everglades Ecosystem of Florida, are cryptic snakes that have long periods of inactivity. In addition, management actions such as removal of every python encountered create challenges for estimating population size and quantifying effects of management using traditional statistical approaches. We used Bayesian analysis of data collected from 59 visual surveys (144 person-surveys) covering a total distance of 485.6 km (1185.1 person-km) and radiotelemetry to estimate detection probability for Burmese Pythons, estimates which can improve interpretation of encounter and removal data. We found that detection probability ranged from 0.0001 0.0146 depending on whether or not efforts units accounted for total human effort across multiple surveyors and statistical method used. Based on our surveys, detection probabilities for Burmese Pythons are therefore likely < 0.05, but factors such as the number of searchers or time of day may improve detection probability. Traditional capture-recapture or visual surveys are, however, unlikely to yield accurate information on Burmese Python population size or trends across time without cost-prohibitive effort. Consequently, novel method development to monitor or measure Burmese Python populations, including techniques better equipped to handle very low detection, is critically needed for informative and reliable inferences about population size or the management effects of python removal.","language":"English","publisher":"BioOne","doi":"10.1670/18-154","usgsCitation":"Nafus, M.G., Mazzotti, F., and Reed, R., 2020, Estimating detection probability for Burmese Pythons with few detections and zero recapture events: Journal of Herpetology, v. 54, no. 1, p. 24-30, https://doi.org/10.1670/18-154.","productDescription":"7 p.","startPage":"24","endPage":"30","ipdsId":"IP-102865","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":376526,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"54","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nafus, Melia G. 0000-0002-7325-3055 mnafus@usgs.gov","orcid":"https://orcid.org/0000-0002-7325-3055","contributorId":197462,"corporation":false,"usgs":true,"family":"Nafus","given":"Melia","email":"mnafus@usgs.gov","middleInitial":"G.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":793269,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mazzotti, Frank J.","contributorId":12358,"corporation":false,"usgs":false,"family":"Mazzotti","given":"Frank J.","affiliations":[{"id":12604,"text":"Department of Wildlife Ecology and Conservation, Fort Lauderdale Research and Education Center, 3205 College Avenue, University of Florida, Davie, FL 33314, USA","active":true,"usgs":false}],"preferred":false,"id":793270,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Reed, Robert 0000-0001-8349-6168 reedr@usgs.gov","orcid":"https://orcid.org/0000-0001-8349-6168","contributorId":152301,"corporation":false,"usgs":true,"family":"Reed","given":"Robert","email":"reedr@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":793271,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70211834,"text":"70211834 - 2020 - Using thermal infrared cameras to detect avian chicks at various distances and vegetative coverages","interactions":[],"lastModifiedDate":"2020-08-07T21:18:11.562582","indexId":"70211834","displayToPublicDate":"2020-01-16T16:15:30","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2287,"text":"Journal of Fish and Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Using thermal infrared cameras to detect avian chicks at various distances and vegetative coverages","docAbstract":"Population monitoring of nesting waterbirds often involves frequent entries into the colony, but alternative methods such as local remotely sensed thermal imaging may help reduce disturbance while providing a cost-effective way to survey breeding populations. Such an approach can have high initial costs, however, which may have reduced the number of studies investigating functionality of paired thermal infrared camera and small unmanned aerial systems. Here, we take the first step of exploring the ability of two thermal infrared cameras to detect an avian chick under varying vegetative cover and distances, preceding field-mounting applications on a small unmanned aerial system. We created seven “bioboxes” to simulate a range of natural vegetation types and densities for a globally important colonial ground-nesting waterbird species, the common tern Sterna hirundo. We placed a juvenile chicken Gallus gallus (surrogate for the locally endangered common tern) in each box, and we tested two market-accessible infrared cameras (produced by FLIR Systems and Infrared Cameras, Inc.) at five elevations using a stationary boom (maximum height = 12 m). We applied computer-based digital thresholding to collected images, identifying pixels meeting one of seven threshold values. The chick was visible from at least one threshold value in 19 and 31 of 35 processed by the FLIR Systems and Infrared Cameras, respectively. Percentage of the chick identified across thresholds was generally highest at lower threshold values and elevations and decreased as elevation and threshold increased; however, the relative importance of each variable changed dramatically across bioboxes and camera types. Ability to detect a chick from processed images generally decreased with increasing elevation, and although we made no quantitative comparisons among boxes, detectability appeared greatest in images from both cameras when little or no vegetation was present. Interestingly, no single threshold value was best for all bioboxes. We observed notable differences between cameras including visual resolution of detected temperature differentials and image processing speed. Results of this controlled study show promise for the use of thermal infrared systems for detecting cryptic species in vegetation. Future research should work to combine thermal infrared and visual sensors with small unmanned aerial systems to test applicability in a mobile field application.
","language":"English","publisher":"Fish and Wildlife Management","doi":"10.3996/072019-JFWM-062","usgsCitation":"Prosser, D., Collier, T., Sullivan, J.D., Dale, K.E., Callahan, C.R., McGowan, P.C., Gaylord, E., Geschke, J.M., Howell, L., Marban, P., and Raman, S., 2020, Using thermal infrared cameras to detect avian chicks at various distances and vegetative coverages: Journal of Fish and Wildlife Management, v. 11, no. 1, p. 245-257, https://doi.org/10.3996/072019-JFWM-062.","productDescription":"13 p.","startPage":"245","endPage":"257","ipdsId":"IP-077752","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":458106,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3996/072019-jfwm-062","text":"Publisher Index Page"},{"id":437159,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P97UT9B7","text":"USGS data release","linkHelpText":"Using Thermal Infrared Cameras to Detect Avian Chicks at Various Distances and Vegetative Coverages"},{"id":377208,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-01-16","publicationStatus":"PW","contributors":{"authors":[{"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":795295,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Collier, Tom","contributorId":208436,"corporation":false,"usgs":false,"family":"Collier","given":"Tom","email":"","affiliations":[{"id":37801,"text":"UASbio","active":true,"usgs":false}],"preferred":false,"id":795296,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sullivan, Jeffery D.","contributorId":202910,"corporation":false,"usgs":false,"family":"Sullivan","given":"Jeffery","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":795297,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dale, Katherine Emily 0000-0002-8544-1571","orcid":"https://orcid.org/0000-0002-8544-1571","contributorId":237786,"corporation":false,"usgs":true,"family":"Dale","given":"Katherine","email":"","middleInitial":"Emily","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":795298,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Callahan, Carl R.","contributorId":205289,"corporation":false,"usgs":false,"family":"Callahan","given":"Carl","email":"","middleInitial":"R.","affiliations":[{"id":37073,"text":"USFWS, Annapolis MD","active":true,"usgs":false}],"preferred":false,"id":795299,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McGowan, Peter C.","contributorId":13867,"corporation":false,"usgs":false,"family":"McGowan","given":"Peter","email":"","middleInitial":"C.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":795300,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gaylord, Edward","contributorId":237787,"corporation":false,"usgs":false,"family":"Gaylord","given":"Edward","email":"","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":795301,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Geschke, Julia M.","contributorId":237788,"corporation":false,"usgs":false,"family":"Geschke","given":"Julia","email":"","middleInitial":"M.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":795302,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Howell, Lucas","contributorId":237789,"corporation":false,"usgs":false,"family":"Howell","given":"Lucas","email":"","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":795303,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Marban, Paul R.","contributorId":221168,"corporation":false,"usgs":false,"family":"Marban","given":"Paul R.","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":795304,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Raman, Saba","contributorId":237790,"corporation":false,"usgs":false,"family":"Raman","given":"Saba","email":"","affiliations":[{"id":7083,"text":"University of Maryland","active":true,"usgs":false}],"preferred":false,"id":795305,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70226268,"text":"70226268 - 2020 - Investigating maternity roost selection by northern long-eared bats at three sites in Wisconsin","interactions":[],"lastModifiedDate":"2023-06-23T14:13:31.31662","indexId":"70226268","displayToPublicDate":"2020-01-16T08:38:06","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1497,"text":"Endangered Species Research","active":true,"publicationSubtype":{"id":10}},"title":"Investigating maternity roost selection by northern long-eared bats at three sites in Wisconsin","docAbstract":"One of the North American bat species most impacted by white-nose syndrome (WNS) is the northern long-eared bat Myotis septentrionalis, which as a result has been listed under the Endangered Species Act. WNS was first detected in Wisconsin in 2014. Unfortunately, little is known regarding the ecology of M. septentrionalis in this state pre-WNS to guide management supporting post-WNS recovery efforts. The objectives of our research were to (1) assess characteristics of trees that are associated with roost tree selection and (2) investigate how characteristics of maternity colony networks compare to colonies in the eastern USA. We mist-netted at 3 sites in Wisconsin in 2015 and 2016, and affixed radio transmitters to 39 female M. septentrionalis. We tracked bats to 53 confirmed day roosts. We found that roost trees were larger, more decayed, and more likely to be in dominant canopy closure areas than random trees. Oaks Quercus spp. were used most frequently and in proportion to their availability in the landscape at 2 field sites, whereas invasive black locust Robinia pseudoacacia was used more than expected based on availability at another site. Overall, minimum convex polygon sizes for maternity roosts were variable (5.2 to 8.9 ha) but similar to values reported for other regions. However, network centrality was low, indicating equitable use of day roosts and more frequent roost switching compared to other regions. Our findings provide information that increasing availability of potential day roosts in the landscape during the reproductive period may improve recruitment, which may in turn mitigate some of the detrimental population effects from WNS.
","language":"English","publisher":"Inter-Research Science Publisher","doi":"10.3354/esr01004","usgsCitation":"Hyzy, B.A., Russell, R.E., Silvis, A., Ford, W., Riddle, J., and Russell, K., 2020, Investigating maternity roost selection by northern long-eared bats at three sites in Wisconsin: Endangered Species Research, v. 41, p. 55-65, https://doi.org/10.3354/esr01004.","productDescription":"11 p., Data release","startPage":"55","endPage":"65","ipdsId":"IP-106190","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":458110,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr01004","text":"Publisher Index Page"},{"id":418323,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YH1668","text":"USGS data release","description":"USGS data release","linkHelpText":"Roost selection for Northern Long-eared Bats (Myotis septentrionalis) in Wisconsin"},{"id":391798,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wisconsin","otherGeospatial":"Black River State Forest, Governor Dodge State Park, Sandhill Wildlife Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.1479721069336,\n 42.996110107947956\n ],\n [\n -90.07038116455078,\n 42.996110107947956\n ],\n [\n -90.07038116455078,\n 43.05785119934999\n ],\n [\n -90.1479721069336,\n 43.05785119934999\n ],\n [\n -90.1479721069336,\n 42.996110107947956\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.89813232421875,\n 44.049102784014536\n ],\n [\n -90.52871704101562,\n 44.049102784014536\n ],\n [\n -90.52871704101562,\n 44.50825885600572\n ],\n [\n -90.89813232421875,\n 44.50825885600572\n ],\n [\n -90.89813232421875,\n 44.049102784014536\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.20736694335936,\n 44.295349956045804\n ],\n [\n -90.11398315429686,\n 44.295349956045804\n ],\n [\n -90.11398315429686,\n 44.39257961837961\n ],\n [\n -90.20736694335936,\n 44.39257961837961\n ],\n [\n -90.20736694335936,\n 44.295349956045804\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"41","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hyzy, Brenna A.","contributorId":171457,"corporation":false,"usgs":false,"family":"Hyzy","given":"Brenna","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":826915,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Russell, Robin E. 0000-0001-8726-7303 rerussell@usgs.gov","orcid":"https://orcid.org/0000-0001-8726-7303","contributorId":3998,"corporation":false,"usgs":true,"family":"Russell","given":"Robin","email":"rerussell@usgs.gov","middleInitial":"E.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":826916,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Silvis, Alex","contributorId":269007,"corporation":false,"usgs":false,"family":"Silvis","given":"Alex","affiliations":[],"preferred":false,"id":826917,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":826918,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Riddle, Jason","contributorId":269008,"corporation":false,"usgs":false,"family":"Riddle","given":"Jason","affiliations":[],"preferred":false,"id":826919,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Russell, Kevin","contributorId":269009,"corporation":false,"usgs":false,"family":"Russell","given":"Kevin","affiliations":[],"preferred":false,"id":826920,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70209182,"text":"70209182 - 2020 - Is your ad hoc model selection strategy affecting your multimodel inference?","interactions":[],"lastModifiedDate":"2020-03-23T07:06:30","indexId":"70209182","displayToPublicDate":"2020-01-16T07:05:31","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Is your ad hoc model selection strategy affecting your multimodel inference?","docAbstract":"(Yackulic) 1.\tEcologists routinely fit complex models with multiple parameters of interest, where hundreds or more competing models are plausible. To limit the number of fitted models, ecologists often define a model selection strategy composed of a series of stages in which certain features of a model are compared while other features are held constant. Defining these multi-stage strategies requires making a series of decisions, which may potentially impact inferences, but have not been critically evaluated.\n2.\tWe begin by identifying key features of strategies, introducing descriptive terms when they did not already exist in the literature. Strategies differ in how they define and order model building stages. Sequential-by-sub-model strategies focus on one sub-model (parameter) at a time with modeling of subsequent sub-models dependent on the selected model structures from the previous stages. Secondary candidate set strategies model sub-models independently and combine the top set of models from each sub-model for selection in a final stage. Build-up approaches define stages across sub-models and increase in complexity at each stage. Strategies also differ in how the top set of models is selected in each stage and whether they use null or more complex model structures for non-target sub-models.\n3.\tWe tested the performance of different model selection strategies using four datasets and three model types. For each dataset, we determined the “true” distribution of AIC weights by fitting all plausible models. Then, we calculated the number of models that would have been fitted and the portion of “true” AIC weight we recovered under different model selection strategies.\n4.\tSequential-by-sub-model strategies often performed poorly. Build-up or secondary candidate sets were more reliable, provided all models within 5 AIC of the top model were carried forward to subsequent stages. The structure of non-target sub-models was less important. \n5.\t Multi-stage approaches cannot compensate for a lack of critical thought in selecting covariates and building models to represent competing a priori hypotheses. However, even when competing hypotheses for different sub-models are limited, thousands or more models may be possible so strategies to explore candidate model space reliably and efficiently will be necessary.","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.2997","usgsCitation":"Morin, D.J., Yackulic, C.B., Diffendorfer, J., Lesmeister, D.B., Nielsen, C., Reid, J., and Schauber, E.M., 2020, Is your ad hoc model selection strategy affecting your multimodel inference?: Ecosphere, v. 11, no. 1, e02997, https://doi.org/10.1002/ecs2.2997.","productDescription":"e02997","ipdsId":"IP-106290","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":458118,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2997","text":"Publisher Index Page"},{"id":373428,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2020-01-16","publicationStatus":"PW","contributors":{"authors":[{"text":"Morin, Dana J.","contributorId":200306,"corporation":false,"usgs":false,"family":"Morin","given":"Dana","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":785265,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yackulic, Charles B. 0000-0001-9661-0724 cyackulic@usgs.gov","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":4662,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","email":"cyackulic@usgs.gov","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":785266,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Diffendorfer, James E. 0000-0003-1093-6948 jediffendorfer@usgs.gov","orcid":"https://orcid.org/0000-0003-1093-6948","contributorId":223504,"corporation":false,"usgs":true,"family":"Diffendorfer","given":"James","email":"jediffendorfer@usgs.gov","middleInitial":"E.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":785267,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lesmeister, Damon B. 0000-0003-1102-0122","orcid":"https://orcid.org/0000-0003-1102-0122","contributorId":205006,"corporation":false,"usgs":false,"family":"Lesmeister","given":"Damon","email":"","middleInitial":"B.","affiliations":[{"id":37019,"text":"USDA Forest Service, Pacific Northwest Research Station","active":true,"usgs":false}],"preferred":false,"id":785268,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nielsen, Clayton","contributorId":223505,"corporation":false,"usgs":false,"family":"Nielsen","given":"Clayton","email":"","affiliations":[{"id":40724,"text":"Cooperative Wildlife Research Laboratory and Department of Forestry, Southern Illinois University, 251 Life Science II, Mail Code 6504, Carbondale, Illinois 62901 USA","active":true,"usgs":false}],"preferred":false,"id":785269,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Reid, Janice","contributorId":89391,"corporation":false,"usgs":false,"family":"Reid","given":"Janice","affiliations":[{"id":6644,"text":"Princeton University","active":true,"usgs":false}],"preferred":false,"id":785270,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schauber, Eric M.","contributorId":223506,"corporation":false,"usgs":false,"family":"Schauber","given":"Eric","email":"","middleInitial":"M.","affiliations":[{"id":40725,"text":"Illinois Natural History Survey, Prairie Research Institute, University of Illinois Urbana-Champaign, 1816 S. Oak St., Champaign, IL 61820 USA","active":true,"usgs":false}],"preferred":false,"id":785271,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70207962,"text":"70207962 - 2020 - Extreme mortality and reproductive failure of common murres resulting from the northeast Pacific marine heatwave of 2014-2016","interactions":[],"lastModifiedDate":"2023-06-23T14:25:48.582192","indexId":"70207962","displayToPublicDate":"2020-01-15T13:49:36","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Extreme mortality and reproductive failure of common murres resulting from the northeast Pacific marine heatwave of 2014-2016","docAbstract":"About 62,000 dead or dying common murres (Uria aalge), the trophically dominant fish-eating seabird of the North Pacific, washed ashore between summer 2015 and spring 2016 on beaches from California to Alaska. Most birds were severely emaciated and, so far, no evidence for anything other than starvation was found to explain this mass mortality. Three-quarters of murres were found in the Gulf of Alaska and the remainder along the West Coast. Studies show that only a fraction of birds that die at sea typically wash ashore, and we estimate that total mortality approached 1 million birds. About two-thirds of murres killed were adults, a substantial blow to breeding populations. Additionally, 22 complete reproductive failures were observed at multiple colonies region-wide during (2015) and after (2016–2017) the mass mortality event. Die-offs and breeding failures occur sporadically in murres, but the magnitude, duration and spatial extent of this die-off, associated with multi-colony and multi-year reproductive failures, is unprecedented and astonishing. These events co-occurred with the most powerful marine heatwave on record that persisted through 2014–2016 and created an enormous volume of ocean water (the “Blob”) from California to Alaska with temperatures that exceeded average by 2–3 standard deviations. Other studies indicate that this prolonged heatwave reduced phytoplankton biomass and restructured zooplankton communities in favor of lower-calorie species, while it simultaneously increased metabolically driven food demands of ectothermic forage fish. In response, forage fish quality and quantity diminished. Similarly, large ectothermic groundfish were thought to have increased their demand for forage fish, resulting in greater top-predator demands for diminished forage fish resources. We hypothesize that these bottom-up and top-down forces created an “ectothermic vise” on forage species leading to their system-wide scarcity and resulting in mass mortality of murres and many other fish, bird and mammal species in the region during 2014–2017.
Resource managers may be hesitant to make decisions based on environmental (e)DNA results alone since eDNA is an indirect method of species detection. One way to reduce the uncertainty of eDNA is to identify laboratory‐based protocols that ensure repeatable and reproducible results. We conducted a double‐blind round‐robin analysis of probe‐based assays for DNA of dreissenid (Dreissena spp.) mussels, which are prolific aquatic invaders that can cause significant economic and ecological impacts. DNA extract from water samples spiked with known amounts of dreissenid DNA and from water samples collected from waters with and without dreissenids were analyzed by four independent research laboratories. We used results to calculate detection repeatability within laboratories and assays, detection reproducibility among laboratories and assays, and estimated dreissenid DNA copy number precision and accuracy. Laboratory and assay repeatability and reproducibility of detection results were high, 91% and 92%, respectively. The estimated copy numbers were neither precise nor accurate for samples spiked with <773 gene copies. These results suggest that eDNA surveillance of dreissenid mussels, using the protocols evaluated herein, can generate reliable detection data for decision‐making. However, managers should be cautious about using the quantitative information often associated with eDNA detections, especially when DNA is at lower abundance. Our results provide strong support that eDNA has the potential to provide repeatable and reproducible evidence under varying laboratory conditions and for different sample water chemistries. This is reassuring since the demand for eDNA surveillance is widespread and number of laboratories that process eDNA samples is growing steadily.
","language":"English","publisher":"Wiley","doi":"10.1002/edn3.68","usgsCitation":"Sepulveda, A.J., Hutchins, P.R., Jackson, C., Ostberg, C.O., Laramie, M., Amberg, J., Counihan, T., Hoegh, A.B., and Pilliod, D.S., 2020, A round-robin evaluation of the repeatability and reproducibility of environmental DNA assays for dreissenid mussels: Environmental DNA, v. 2, no. 4, p. 446-459, https://doi.org/10.1002/edn3.68.","productDescription":"14 p.","startPage":"446","endPage":"459","ipdsId":"IP-111602","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":458141,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/edn3.68","text":"Publisher Index Page"},{"id":437164,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NMZZNP","text":"USGS data release","linkHelpText":"PCR results from dreissenid mussel round robin assay analyses, 2018-2019"},{"id":371541,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Montana, Nevada, New York, Oregon, Washington, Wisconsin, Wyoming","otherGeospatial":"Columbia River, Jackson Lake, Lake Mead, Lake Michigan, San Justo Reservoir, Seneca Lake, Yellowstone River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -87.6708984375,\n 42.16340342422401\n ],\n [\n -86.8359375,\n 42.16340342422401\n ],\n [\n -86.8359375,\n 44.809121700077355\n ],\n [\n -87.6708984375,\n 44.809121700077355\n ],\n [\n -87.6708984375,\n 42.16340342422401\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.92248535156249,\n 36.005784428799934\n ],\n [\n 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phutchins@usgs.gov","orcid":"https://orcid.org/0000-0001-5232-0821","contributorId":198337,"corporation":false,"usgs":true,"family":"Hutchins","given":"Patrick","email":"phutchins@usgs.gov","middleInitial":"R.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":780181,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jackson, Craig 0000-0003-4023-0276 cjackson@usgs.gov","orcid":"https://orcid.org/0000-0003-4023-0276","contributorId":192276,"corporation":false,"usgs":true,"family":"Jackson","given":"Craig","email":"cjackson@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":780182,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ostberg, Carl O. 0000-0003-1479-8458","orcid":"https://orcid.org/0000-0003-1479-8458","contributorId":220731,"corporation":false,"usgs":true,"family":"Ostberg","given":"Carl","middleInitial":"O.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":780183,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Laramie, Matthew 0000-0001-7820-2583 mlaramie@usgs.gov","orcid":"https://orcid.org/0000-0001-7820-2583","contributorId":152532,"corporation":false,"usgs":true,"family":"Laramie","given":"Matthew","email":"mlaramie@usgs.gov","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":780184,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Amberg, Jon 0000-0002-8351-4861 jamberg@usgs.gov","orcid":"https://orcid.org/0000-0002-8351-4861","contributorId":149785,"corporation":false,"usgs":true,"family":"Amberg","given":"Jon","email":"jamberg@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":780185,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Counihan, Timothy D. 0000-0003-4967-6514","orcid":"https://orcid.org/0000-0003-4967-6514","contributorId":207532,"corporation":false,"usgs":true,"family":"Counihan","given":"Timothy D.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":780186,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hoegh, Andrew B.","contributorId":166684,"corporation":false,"usgs":false,"family":"Hoegh","given":"Andrew","email":"","middleInitial":"B.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":780271,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Pilliod, David S. 0000-0003-4207-3518","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":216342,"corporation":false,"usgs":true,"family":"Pilliod","given":"David","middleInitial":"S.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":780187,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70225829,"text":"70225829 - 2020 - Using the Lomb-Scargle method for wave statistics from gappy time series","interactions":[],"lastModifiedDate":"2021-11-10T14:50:24.002282","indexId":"70225829","displayToPublicDate":"2020-01-13T08:43:38","publicationYear":"2020","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Using the Lomb-Scargle method for wave statistics from gappy time series","docAbstract":"Sandwich Town Neck Beach in Sandwich, MA, has experienced substantial erosion and has been the subject of efforts by the town and private landowners to limit the sand loss. Erosion has been particularly dramatic in the past five years with the loss of dwellings. Sandwich's nourishment efforts presented a unique opportunity for scientists at the U.S. Geological Survey Woods Hole Coastal and Marine Science Center to monitor beach morphology and to test new technologies and techniques such as geo-referenced drone imaging. Two bottom lander deployments were performed in Cape Cod Bay at a location that was key to model the fate of waves at Sandwich Town Neck Beach and to support the study of beach morphological evolution. The study period was after the town nourished the beach and during a time when several intense winter storms reshaped the beach and removed much of the nourished sand. A TRDI Workhorse Sentinel V ADCP was used for both deployments. For wave bursts, the instruments collected 2048 samples at 2 Hz every hour. The first deployment during the winter of 2016 returned good quality data. The second deployment during the following winter had gaps throughout the time series from a wiring problem in the external battery pack. The timing of the gaps was random, the duration approximately 100 s. While most of the bursts started at the top of each hour, many had 1-3 gaps within. Time series data with random gaps are problematic for computing spectral density, and thus, wave statistics. This kind of situation is familiar in other scientific disciplines such as astrophysics [1], where techniques exist to find stationary signals in sparse data. One of these methods is the Lomb-Scargle technique for computing periodograms. The most useful feature of the Lomb-Scargle (LS) method is that it allows the spectral analysis of incomplete records, without having to manipulate the record to extrapolate from or replace missing data. We compared the effectiveness of LS against common methods of averaging Fourier transforms such as a simple un-windowed Fast Fourier transform (FFT), Welch's method, and TRDI's Wavesmon software; methods that are commonly used in oceanography for non-gappy data. Synthetic data series that have been artificially modified to introduce gaps were used to evaluate the performance of each method. The LS approach was able to recover spectral density even with several 100-s gaps present. The method was applied here to the gappy and non-gappy data from both Sandwich deployments, and wave statistics were obtained and compared to the wave-buoy data. LS was used to process data that contains gaps that was rejected by Wavesmon, which was approximately 39% of the dataset. Significant wave height and peak period from LS compared well with buoy data. Mean period computed on gappy data using LS produced values biased low, compared with other methods when gaps were filled with the mean value. The LS technique has potential to uncover low-frequency signals such as infragravity waves from gappy records where the non-gappy segments are not long enough to resolve them. It has potential to unlock new information from older data sets.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"2019 IEEE/OES twelfth current, waves and turbulence measurement (CWTM)","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"IEEE Oceanic Engineering Society - Current, Waves, Turbulence and Measurement Applications Workshop","conferenceDate":"Mar 10-13, 2019","language":"English","publisher":"IEEE","doi":"10.1109/CWTM43797.2019.8955285","usgsCitation":"Martini, M.A., Aretxabaleta, A., and Sherwood, C.R., 2020, Using the Lomb-Scargle method for wave statistics from gappy time series, in 2019 IEEE/OES twelfth current, waves and turbulence measurement (CWTM), Mar 10-13, 2019, 9 p., https://doi.org/10.1109/CWTM43797.2019.8955285.","productDescription":"9 p.","ipdsId":"IP-105269","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":391573,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","city":"Sandwich","otherGeospatial":"Sandwich Town Neck Beach","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.48888206481934,\n 41.762413206292656\n ],\n [\n -70.47154426574707,\n 41.762413206292656\n ],\n [\n -70.47154426574707,\n 41.77297600540535\n ],\n [\n -70.48888206481934,\n 41.77297600540535\n ],\n [\n -70.48888206481934,\n 41.762413206292656\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Martini, Marinna A. 0000-0002-7757-5158 mmartini@usgs.gov","orcid":"https://orcid.org/0000-0002-7757-5158","contributorId":2456,"corporation":false,"usgs":true,"family":"Martini","given":"Marinna","email":"mmartini@usgs.gov","middleInitial":"A.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":826574,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aretxabaleta, Alfredo 0000-0002-9914-8018 aaretxabaleta@usgs.gov","orcid":"https://orcid.org/0000-0002-9914-8018","contributorId":140090,"corporation":false,"usgs":true,"family":"Aretxabaleta","given":"Alfredo","email":"aaretxabaleta@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":826575,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Sherwood, Christopher R. 0000-0001-6135-3553 csherwood@usgs.gov","orcid":"https://orcid.org/0000-0001-6135-3553","contributorId":2866,"corporation":false,"usgs":true,"family":"Sherwood","given":"Christopher","email":"csherwood@usgs.gov","middleInitial":"R.","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":826576,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70208949,"text":"70208949 - 2020 - Using a dense seismic array to determine structure and site effects of the Two Towers earthflow in northern California","interactions":[],"lastModifiedDate":"2020-03-09T06:45:36","indexId":"70208949","displayToPublicDate":"2020-01-08T06:43:41","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Using a dense seismic array to determine structure and site effects of the Two Towers earthflow in northern California","docAbstract":"We deployed a network of 68 three-component geophones on the slow moving Two\n\tTowers earthflow in northern California. We compute horizontal-to-vertical spectral ratios\n\t(HVSRs) from the ambient seismic field. The HVSRs have two prominent peaks, one near\n\t1.23 Hz and another between 4 and 8 Hz at most stations. The 1.23 Hz resonance is a property of the background noise field and may be due to a velocity contrast at a few hundred\n\tmeters depth. We interpret the higher frequency peaks as being related to slide deposits and invert the spectral ratios for shallow velocity structure using in situ thickness measurements\n\tas a priori constraints on the inversion. The thickness of the shallowest, low-velocity layer\n\tis systematically larger than landslide thicknesses inferred from inclinometer data acquired\n\tsince 2013. Given constraints from field observations and boreholes, the inversion may reflect the thickness of deposits of an older slide that is larger in spatial extent and depth than\n\tthe currently active slide. Because the HVSR peaks measured at Two Towers are caused by shallow slide deposits and represent frequencies that will experience amplification during\n\tearthquakes, the depth of the actively sliding mass may be less relevant for assessing potential slide volume and associated hazard than the thicknesses determined by our inversions.\n\tMore generally, our results underscore the utility of combining both geotechnical measurements and subsurface imaging for landslide characterization and hazard assessment.","language":"English","publisher":"GSW","doi":"10.1785/0220190206","usgsCitation":"Thomas, A.M., Spica, Z., Bodmer, M., Schulz, W.H., and Roering, J., 2020, Using a dense seismic array to determine structure and site effects of the Two Towers earthflow in northern California: Seismological Research Letters, v. 91, no. 2A, p. 913-920, https://doi.org/10.1785/0220190206.","productDescription":"8 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environment","interactions":[],"lastModifiedDate":"2020-07-16T18:41:56.104549","indexId":"70211192","displayToPublicDate":"2020-01-07T13:27:15","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2496,"text":"Journal of Virological Methods","active":true,"publicationSubtype":{"id":10}},"title":"Field-based method for assessing duration of infectivity for influenza A viruses in the environment","docAbstract":"Understanding influenza A virus (IAV) persistence in wetlands is limited by a paucity of field studies relating to the maintenance of infectivity over time. The duration of IAV infectivity in water has been assessed under variable laboratory conditions, but results are difficult to translate to more complex field conditions. We tested a field-based method to assess the viability of IAVs in an Alaska wetland during fall and winter which incorporated physical and chemical properties of the waterbody in which samples were held. Filtered pond water was inoculated with avian fecal samples collected from the environment, aliquoted into a series of duplicate sealed vials and submerged back in the wetland for up to 132 days (October 2018–March 2019). Sample aliquots were sequentially recovered and tested for IAVs by rRT-PCR and virus isolation. One sample remained rRT-PCR positive for the duration of the study and virus isolation positive for 118 days. The surrounding water temperature was 1°–6 °C with near neutral pH (6.6–7.3) for the duration of the study. This proof of concept study demonstrates a protocol for testing the persistence of infectious IAV naturally shed from waterfowl under ambient environmental conditions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jviromet.2020.113818","usgsCitation":"Reeves, A.B., Ramey, A.M., Koch, J.C., Poulson, R., and Stallknecht, D., 2020, Field-based method for assessing duration of infectivity for influenza A viruses in the environment: Journal of Virological Methods, v. 277, 113818, https://doi.org/10.1016/j.jviromet.2020.113818.","productDescription":"113818","ipdsId":"IP-112830","costCenters":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"links":[{"id":458201,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9289940","text":"External Repository"},{"id":437177,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9B8XH6E","text":"USGS data release","linkHelpText":"Influenza A Virus Persistence Data from an Urban Wetland in Anchorage, Alaska, 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\"}}]}","volume":"277","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Reeves, Andrew B. 0000-0002-7526-0726 areeves@usgs.gov","orcid":"https://orcid.org/0000-0002-7526-0726","contributorId":167362,"corporation":false,"usgs":true,"family":"Reeves","given":"Andrew","email":"areeves@usgs.gov","middleInitial":"B.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":793051,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"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":793052,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Koch, Joshua C. 0000-0001-7180-6982 jkoch@usgs.gov","orcid":"https://orcid.org/0000-0001-7180-6982","contributorId":202532,"corporation":false,"usgs":true,"family":"Koch","given":"Joshua","email":"jkoch@usgs.gov","middleInitial":"C.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":120,"text":"Alaska Science Center Water","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":793053,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Poulson, Rebecca L.","contributorId":198807,"corporation":false,"usgs":false,"family":"Poulson","given":"Rebecca L.","affiliations":[{"id":7125,"text":"Southeastern Cooperative Wildlife Disease Study, College of Veterinary Medicine, University of Georgia, Athens, GA 30602, USA.","active":true,"usgs":false}],"preferred":false,"id":793054,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Stallknecht, David E.","contributorId":225107,"corporation":false,"usgs":false,"family":"Stallknecht","given":"David E.","affiliations":[{"id":36701,"text":"Southeastern Cooperative Wildlife Disease Study, Department of Population Health, College of Veterinary Medicine, University of Georgia","active":true,"usgs":false}],"preferred":false,"id":793055,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70210790,"text":"70210790 - 2020 - Petrologic insights into rift zone magmatic interactions from the 2011 eruption of Kīlauea Volcano, Hawaiʻi","interactions":[],"lastModifiedDate":"2020-06-25T14:54:58.664449","indexId":"70210790","displayToPublicDate":"2020-01-07T09:50:29","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2420,"text":"Journal of Petrology","active":true,"publicationSubtype":{"id":10}},"title":"Petrologic insights into rift zone magmatic interactions from the 2011 eruption of Kīlauea Volcano, Hawaiʻi","docAbstract":"The high frequency of historical eruptions at Kīlauea Volcano presents an exceptional opportunity to address fundamental questions related to the transport, storage, and interaction of magmas within rift zones. The Nāpau Crater area on Kīlauea’s East Rift Zone (ERZ) experienced nine fissure eruptions within 50 years (1961–2011). Most of the magma intruded during these frequent eruptions remained stored within the rift zone, creating a potential magma mixing depot within the ERZ. The superbly monitored and sampled 2011 eruption (Puʻu ʻŌʻō episode 59) presents an extraordinary opportunity to evaluate magma mixing processes within the ERZ. Whole-rock, glass, and olivine compositions were determined, not only for lava from the 2011 eruption, but also for a new suite of Nāpau Crater area samples from the 1963, 1965, 1968, 1983, and 1997 eruptions, as well as the previously undocumented 1922 eruption. Whole-rock XRF data revealed two geochemically distinct magma batches for episode 59: one less evolved (∼6·6 wt % MgO, 0·46 wt % K2O) than the other (∼6·2 wt % MgO, 0·58 wt % K2O). Episode 59 lava is remarkably aphyric (∼0·1 vol. % phenocrysts), making use of mineralogy to identify parent magma affinities problematic. Linear compositional trends of whole-rock major and trace elements, and reversely zoned olivine crystals indicate episode 59 lavas underwent magma mixing. Least squares regression calculations and plots of major and trace element data, were used to evaluate whether the episode 59 samples are products of mixing summit-derived magma with residual magma from previous Nāpau Crater area eruptions. The regression results and trace element ratios are inconsistent with previously proposed mixing scenarios, but they do support mixing between summit-derived magma and residual magma from the 1983 and 1997 Nāpau Crater area eruptions. These magmas were stored in physically and chemically distinct pods at depths of 1·6–3·0 km prior to mixing with new magma intruded from the summit to produce the episode 59 lava. One pod contained a fractionated equivalent of 1983 lava, and the other a hybrid of compositions similar to 1983 and 1997 lavas. The petrology of episode 59 lava demonstrates that magmas from two previous eruptions (1983 and 1997) were available to mix with magma intruded from the summit region. This study clarifies the pre-eruptive history of the mixed episode 59 lava, and elucidates the evolution of the volcano's magmatic system in a region of frequent eruptions.","language":"English","publisher":"Oxford University Press","doi":"10.1093/petrology/egz064","usgsCitation":"Walker, B.H., Garcia, M.O., and Orr, T.R., 2020, Petrologic insights into rift zone magmatic interactions from the 2011 eruption of Kīlauea Volcano, Hawaiʻi: Journal of Petrology, v. 60, no. 11, p. 2051-2075, https://doi.org/10.1093/petrology/egz064.","productDescription":"25 p.","startPage":"2051","endPage":"2075","ipdsId":"IP-091040","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":458203,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/petrology/egz064","text":"Publisher Index Page"},{"id":375917,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"East Rift Zone","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -155.3521728515625,\n 19.16592425362802\n ],\n [\n -155.01708984375,\n 19.16592425362802\n ],\n [\n -155.01708984375,\n 19.33706180106996\n ],\n [\n -155.3521728515625,\n 19.33706180106996\n ],\n [\n -155.3521728515625,\n 19.16592425362802\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"60","issue":"11","noUsgsAuthors":false,"publicationDate":"2020-01-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Walker, Brett H.","contributorId":225523,"corporation":false,"usgs":false,"family":"Walker","given":"Brett","email":"","middleInitial":"H.","affiliations":[{"id":36402,"text":"University of Hawaii","active":true,"usgs":false}],"preferred":false,"id":791433,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Garcia, Michael O.","contributorId":225524,"corporation":false,"usgs":false,"family":"Garcia","given":"Michael","email":"","middleInitial":"O.","affiliations":[{"id":36402,"text":"University of Hawaii","active":true,"usgs":false}],"preferred":false,"id":791434,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Orr, Tim R. 0000-0003-1157-7588 torr@usgs.gov","orcid":"https://orcid.org/0000-0003-1157-7588","contributorId":149803,"corporation":false,"usgs":true,"family":"Orr","given":"Tim","email":"torr@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":791435,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70211340,"text":"70211340 - 2020 - Using conceptual models to relate multiparameter satellite data to subsurface volcanic processes in Latin America","interactions":[],"lastModifiedDate":"2020-09-01T13:54:44.456524","indexId":"70211340","displayToPublicDate":"2020-01-05T10:07:56","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1757,"text":"Geochemistry, Geophysics, Geosystems","active":true,"publicationSubtype":{"id":10}},"title":"Using conceptual models to relate multiparameter satellite data to subsurface volcanic processes in Latin America","docAbstract":"Satellite data have been extensively used to identify volcanic behavior. However, the physical subsurface processes causing any individual manifestation of activity can be ambiguous. We propose a classification scheme for the cause of unrest that simultaneously considers three multiparameter satellite observations. The scheme is based on characteristics of the volcanic system (open, closed, and eruptive) and unrest mechanisms (intrusion, evolution, and withdrawal) occurring at shallow depths in the volcanic system. We applied these models to satellite observations acquired at 47 of the most active volcanoes in Latin America. Of the volcanoes studied, 44 had a robust enough dataset for classification and were clustered into 4 groups and 10 subgroups with common behavioral characteristics. By identifying that these volcanoes can be clustered into a number of groupings significantly less than the number of volcanoes, we have demonstrated that commonalities in behavior patterns exist among diverse volcanic systems. Identifying volcanoes with similar characteristics underpins the use of past observations at one volcano to forecast activity at another and diverges from typical volcanic groupings, which are focused on geologic parameters (i.e., composition, volcano type, and tectonic setting). Based on satellite data alone, we have identified preeruptive intrusion prior to 15 eruptions at 12 different volcanoes, magma evolution prior to 18 eruptions at 13 volcanoes, and magma withdrawal at 3 eruptions and 3 volcanoes. Improvements to the spatial and temporal resolution are needed to make these relations robust. This classification scheme provides a framework for future automated clustering of volcanoes.
A common idea in the discussion of soil carbon processes is that litter decomposition rates and soil carbon stocks are inversely related. To test this overall hypothesis, simultaneous studies were conducted of the relationship of environmental gradients to leaf and wood decomposition, buried cloth decomposition and percent soil organic matter in Taxodium distichum swamps across the Mississippi River Alluvial Valley (MRAV) and northern Gulf of Mexico (GOM) of the US. Decomposition of leaf tissue was 6.2 to 10.9 times faster than wood tissue. Both precipitation and flooding gradients were negatively related to leaf and wood litter decomposition rates based on models developed using Stepwise General Model Selection (MRAV vs. GOM, respectively). Cotton cloth should not be used as a proxy for plant litter without prior testing because cloth responded differently than plant litter to regional environmental gradients in T. distichum swamps. The overall hypothesis was supported in the MRAV because environments with higher precipitation (climate normal) had lower rates of decomposition and higher percent soil organic matter. In the MRAV, higher levels of percent soil organic matter were related to increased 30-year climate normals (30 year averages of precipitation and air temperature comprising southward increasing PrinComp1). Soil organic carbon % in inland vs. coastal T. distichum forests of the MRAV were comparable (range = 1.5% to 26.9% vs. 9.8 to 31.5%, respectively). GOM swamps had lower rates of litter decomposition in more flooded environments. Woody T. distichum detritus had a half-life of up to 300 years in the MRAV, which points to its likely role in the maintenance of inland “teal” soil organic carbon. This unique study can contribute to the discussion of approaches to maintain environments conducive to soil carbon stock maximization.
","language":"English","publisher":"PLOS","doi":"10.1371/journal.pone.0226998","usgsCitation":"Middleton, B.A., 2020, Trends of litter decomposition and soil organic matter stocks across forested swamp environments of the southeastern US: PLoS ONE, v. 15, no. 1, e0226998, 23 p., https://doi.org/10.1371/journal.pone.0226998.","productDescription":"e0226998, 23 p.","ipdsId":"IP-085013","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":458237,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0226998","text":"Publisher Index Page"},{"id":371401,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Florida, Illinois, Louisiana, Mississippi, Missouri, Texas","otherGeospatial":"Mississippi River Alluvial Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.24218749999999,\n 37.78808138412046\n ],\n [\n -90.17578124999999,\n 37.055177106660814\n ],\n [\n -91.7578125,\n 34.52466147177172\n ],\n [\n -92.5048828125,\n 30.977609093348686\n ],\n [\n -90.2197265625,\n 28.65203063036226\n ],\n [\n -88.9013671875,\n 29.036960648558267\n ],\n [\n -89.20898437499999,\n 29.84064389983441\n ],\n [\n -91.01074218749999,\n 31.203404950917395\n ],\n [\n -88.06640625,\n 37.055177106660814\n ],\n [\n -88.24218749999999,\n 37.78808138412046\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.15234375,\n 29.38217507514529\n ],\n [\n -93.8232421875,\n 29.38217507514529\n ],\n [\n -93.8232421875,\n 31.240985378021307\n ],\n [\n -96.15234375,\n 31.240985378021307\n ],\n [\n -96.15234375,\n 29.38217507514529\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.814453125,\n 29.458731185355344\n ],\n [\n -83.583984375,\n 29.458731185355344\n ],\n [\n -83.583984375,\n 30.56226095049944\n ],\n [\n -84.814453125,\n 30.56226095049944\n ],\n [\n -84.814453125,\n 29.458731185355344\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-01-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Middleton, Beth A. 0000-0002-1220-2326 middletonb@usgs.gov","orcid":"https://orcid.org/0000-0002-1220-2326","contributorId":2029,"corporation":false,"usgs":true,"family":"Middleton","given":"Beth","email":"middletonb@usgs.gov","middleInitial":"A.","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":779850,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70211922,"text":"70211922 - 2020 - Estimating bedload from suspended load and water discharge in sand bed rivers","interactions":[],"lastModifiedDate":"2020-08-11T20:13:57.981854","indexId":"70211922","displayToPublicDate":"2020-01-03T15:10:09","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Estimating bedload from suspended load and water discharge in sand bed rivers","docAbstract":"Estimates of fluvial sediment discharge from in situ instruments are an important component of large‐scale sediment budgets that track long‐term geomorphic change. Suspended sediment load can be reliably estimated using acoustic or physical sampling techniques; however, bedload is difficult to measure directly and can consequently be one of the largest sources of uncertainty in estimates of total load. We propose a physically informed predictive empirical model for bedload sand flux as a function of variables that are measured using existing acoustic or physical sampling techniques. This model depends on the assumption that concentration and grain size in suspension are in equilibrium with reach‐averaged boundary conditions. Bayesian inference is used to fit model parameters to data from eight sand‐bed rivers and to simulate bedload flux over the available gage record at one site on the Colorado River in Grand Canyon National Park. We find that the cumulative bedload flux during the 9 year period from 2008 to 2016 was 5% of the cumulative suspended sand load; however, instantaneous bedload flux ranged from as little as 1% of instantaneous suspended sand load to as much as 75% of instantaneous suspended sand load due to fluctuations in flow strength and sediment supply. Changes in bedload flux at a constant discharge are indicative of short‐term sediment supply enrichment and depletion. Long‐term average bedload flux cannot be expected to remain constant in the future as the river adjusts to changes in sediment runoff and the dam‐regulated discharge regime.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019WR025883","usgsCitation":"Ashley, T., McElroy, B., Buscombe, D., Grams, P.E., and Kaplinski, M., 2020, Estimating bedload from suspended load and water discharge in sand bed rivers: Water Resources Research, v. 56, no. 2, e2019WR025883, 25 p., https://doi.org/10.1029/2019WR025883.","productDescription":"e2019WR025883, 25 p.","ipdsId":"IP-108262","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":458242,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/essoar.10503756.1","text":"External Repository"},{"id":377386,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Colorado River, Grand Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.005126953125,\n 35.71083783530009\n ],\n [\n -111.37390136718749,\n 35.71083783530009\n ],\n [\n -111.37390136718749,\n 36.92793899776678\n ],\n [\n -114.005126953125,\n 36.92793899776678\n ],\n [\n -114.005126953125,\n 35.71083783530009\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"56","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-02-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Ashley, T.C.","contributorId":238017,"corporation":false,"usgs":false,"family":"Ashley","given":"T.C.","email":"","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":795824,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McElroy, B.","contributorId":23797,"corporation":false,"usgs":true,"family":"McElroy","given":"B.","email":"","affiliations":[],"preferred":false,"id":795825,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buscombe, D.","contributorId":44020,"corporation":false,"usgs":true,"family":"Buscombe","given":"D.","email":"","affiliations":[],"preferred":false,"id":795826,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grams, Paul E. 0000-0002-0873-0708","orcid":"https://orcid.org/0000-0002-0873-0708","contributorId":216115,"corporation":false,"usgs":true,"family":"Grams","given":"Paul","middleInitial":"E.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":795827,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kaplinski, M.","contributorId":31576,"corporation":false,"usgs":true,"family":"Kaplinski","given":"M.","email":"","affiliations":[],"preferred":false,"id":795828,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70208092,"text":"70208092 - 2020 - Integrating multiple data sources and multi-scale land-cover data to model the distribution of a declining amphibian","interactions":[],"lastModifiedDate":"2020-01-27T19:59:37","indexId":"70208092","displayToPublicDate":"2019-12-30T19:58:43","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Integrating multiple data sources and multi-scale land-cover data to model the distribution of a declining amphibian","docAbstract":"Determining the spatial scale at which landscape features influence population persistence is an important task for conservation planning. One challenge is that sampling biases confound factors that influence species occurrence and survey effort. Recent developments in Point Process Models (PPMs) enable researchers to disentangle the sampling process from ecological drivers of species' distributions. Land-cover change is a driver of decline for the western spadefoot (Spea hammondii), which has been extirpated from much of its range in California. Assessing this species' status requires information on the current distribution of suitable habitat within its historical range, but little is known about the effect of the landscape surrounding breeding ponds on spadefoot occurrence. Critically, surveys for western spadefoots often occur along roads, potentially biasing data used to fit species distribution models. We created PPMs integrating historical presence/non-detection and presence-only data for western spadefoots and land-cover data at multiple spatial scales to model the distribution of this species while removing the influence of sampling bias. There was spatial sampling bias in presence-only data; records were more likely to be reported near roads and urban centers and PPMs that removed sampling bias outperformed models that ignored sampling bias. The occurrence of western spadefoots was positively related to the proportion of grassland within a 2000 m buffer. The remaining habitat for western spadefoots is largely found in the foothills surrounding California's Central Valley. Our study illustrates how PPMs can improve projections of habitat suitability and our understanding of the drivers of species' distributions.","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2019.108374","usgsCitation":"Rose, J.P., Halstead, B., and Fisher, R.N., 2020, Integrating multiple data sources and multi-scale land-cover data to model the distribution of a declining amphibian: Biological Conservation, v. 241, 108374, https://doi.org/10.1016/j.biocon.2019.108374.","productDescription":"108374","ipdsId":"IP-108816","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":458282,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.biocon.2019.108374","text":"Publisher Index Page"},{"id":371628,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California ","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.14599609375001,\n 40.96330795307353\n ],\n [\n -123.06884765625,\n 41.062786068733026\n ],\n [\n -123.15673828124999,\n 39.13006024213511\n ],\n [\n -120.21240234375001,\n 35.06597313798418\n ],\n [\n -117.83935546874999,\n 34.17999758688084\n ],\n [\n -117.00439453125,\n 34.994003757575776\n ],\n [\n -117.97119140625,\n 36.06686213257888\n ],\n [\n -119.2236328125,\n 37.77071473849609\n ],\n [\n -122.14599609375001,\n 40.96330795307353\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"241","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Rose, Jonathan P. 0000-0003-0874-9166 jprose@usgs.gov","orcid":"https://orcid.org/0000-0003-0874-9166","contributorId":199339,"corporation":false,"usgs":true,"family":"Rose","given":"Jonathan","email":"jprose@usgs.gov","middleInitial":"P.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":780445,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Halstead, Brian J. 0000-0002-5535-6528 bhalstead@usgs.gov","orcid":"https://orcid.org/0000-0002-5535-6528","contributorId":3051,"corporation":false,"usgs":true,"family":"Halstead","given":"Brian J.","email":"bhalstead@usgs.gov","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":780444,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fisher, Robert N. 0000-0002-2956-3240 rfisher@usgs.gov","orcid":"https://orcid.org/0000-0002-2956-3240","contributorId":1529,"corporation":false,"usgs":true,"family":"Fisher","given":"Robert","email":"rfisher@usgs.gov","middleInitial":"N.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":780446,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70208469,"text":"70208469 - 2020 - Microbial source tracking (MST) in Chattahoochee River National Recreation Area: Seasonal and precipitation trends in MST marker concentrations, and associations with E. coli levels, pathogenic marker presence, and land use","interactions":[],"lastModifiedDate":"2020-02-11T10:05:32","indexId":"70208469","displayToPublicDate":"2019-12-26T10:04:22","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3716,"text":"Water Research","onlineIssn":"1879-2448","printIssn":"0043-1354","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Microbial source tracking (MST) in Chattahoochee River National Recreation Area: Seasonal and precipitation trends in MST marker concentrations, and associations with E. coli levels, pathogenic marker presence, and land use","title":"Microbial source tracking (MST) in Chattahoochee River National Recreation Area: Seasonal and precipitation trends in MST marker concentrations, and associations with E. coli levels, pathogenic marker presence, and land use","docAbstract":"Escherichia coli levels in recreational waters are often used to predict when fecal-associated pathogen levels are a human health risk. The reach of the Chattahoochee River that flows through the Chattahoochee River National Recreation Area (CRNRA), located in the Atlanta-metropolitan area, is a popular recreation area that frequently exceeds the U.S. Environmental Protection Agency beach action value (BAV) for E. coli. A BacteriALERT program has been implemented to provide real-time E. coli estimates in the reach and notify the public of potentially harmful levels of fecal-associated pathogens as indicated by surrogate models based on real-time turbidity measurements from continuous water quality monitoring stations. However, E. coli does not provide information about the sources of fecal contamination and its accuracy as a human health indicator is questionable when sources of contamination are non-human. The objectives of our study were to investigate, within the Park and surrounding watersheds, seasonal and precipitation-related patterns in microbial source tracking marker concentrations of possible sources (human, dog, and ruminant), assess correlations between source contamination levels and culturable E. coli levels, determine which sources best explained model-based E. coli estimates above the BAV and detection of esp2 (a marker for the esp gene associated with pathogenic strains of Enterococcus faecium and Enterococcus faecalis), and investigate associations between source contamination levels and land use features. Three BacteriALERT sites on the Chattahoochee River were sampled six times per season in the winter and summer from December 2015 through September 2017, and 11 additional stream sites (synoptic sites) from the CRNRA watershed were sampled once per season. Samples were screened with microbial source tracking (MST) quantitative PCR (qPCR) markers for humans (HF183 Taqman), dogs (DogBact), and ruminants (Rum2Bac), the esp2 qPCR marker, and culturable E. coli. At the BacteriALERT sites, HF183 Taqman concentrations were higher under wet conditions DogBact concentrations were greater in the winter and under wet conditions, and Rum2Bac concentrations were comparatively low throughout the study with no difference across seasons or precipitation conditions. Concentrations of HF183 Taqman, DogBact, and Rum2Bac were positively correlated with culturable E. coli concentrations; however, DogBact had the largest R2 value among the three markers, and the forward stepwise regression indicated it was the best predictor of culturable E. coli concentrations at the BacteriALERT sites. Recursive partitioning indicated that BAV exceedances of model-based E. coli estimates were best explained by DogBact concentrations ≥3 gene copies per mL (CN/mL). Detections of esp2 at BacteriALERT sites were best explained by DogBact concentrations ≥11 CN/mL, while detections of esp2 at synoptic sites were best explained by HF183 Taqman ≥29 CN/mL. At the synoptic sites, HF183 Taqman levels were associated with wastewater treatment plant density. However, this relationship was driven primarily by a single site, suggesting possible conveyance issues in that catchment. esp2 detections at synoptic sites were positively associated with development within a 2-km radius and negatively associated with development within the catchment, suggesting multiple sources of esp2 in the watershed. DogBact and Rum2Bac were not associated with the land use features included in our analyses. Implications for Park management include: 1) fecal contamination levels were highest during wet conditions and in the off season when fewer visitors are expected to be participating in water-based recreation, 2) dogs are likely contributors to fecal contamination in the CRNRA and may be sources of pathogenic bacteria indicating further investigation of the origins of this contamination may be warranted as would be research to understand the human health risks from exposure to dog fecal contamination, and 3) high levels of the human marker at one site in the CRNRA watershed suggests more extensive monitoring in that catchment may locate the origin of human fecal contamination detected during this study.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2019.115435","usgsCitation":"McKee, A.M., Molina, M., Cyterski, M., and Couch, A., 2020, Microbial source tracking (MST) in Chattahoochee River National Recreation Area: Seasonal and precipitation trends in MST marker concentrations, and associations with E. coli levels, pathogenic marker presence, and land use: Water Research, v. 171, 115435, 12 p., https://doi.org/10.1016/j.watres.2019.115435.","productDescription":"115435, 12 p.","ipdsId":"IP-105660","costCenters":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"links":[{"id":458294,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.watres.2019.115435","text":"Publisher Index Page"},{"id":437182,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P957P46S","text":"USGS data release","linkHelpText":"Microbial Source Tracking Marker Concentrations in the Chattahoochee River National Recreation Area Watershed in 2015-2017, Georgia, USA"},{"id":372227,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia","otherGeospatial":"Chattahoochee River National Recreation Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.51370239257812,\n 33.90347621404078\n ],\n [\n -83.91769409179688,\n 33.90347621404078\n ],\n [\n -83.91769409179688,\n 34.250405862125\n ],\n [\n -84.51370239257812,\n 34.250405862125\n ],\n [\n -84.51370239257812,\n 33.90347621404078\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"171","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"McKee, Anna M. 0000-0003-2790-5320 amckee@usgs.gov","orcid":"https://orcid.org/0000-0003-2790-5320","contributorId":166725,"corporation":false,"usgs":true,"family":"McKee","given":"Anna","email":"amckee@usgs.gov","middleInitial":"M.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":782032,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Molina, Marirosa","contributorId":220538,"corporation":false,"usgs":false,"family":"Molina","given":"Marirosa","email":"","affiliations":[{"id":13529,"text":"US Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":782033,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cyterski, Mike","contributorId":222389,"corporation":false,"usgs":false,"family":"Cyterski","given":"Mike","email":"","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":782034,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Couch, Ann","contributorId":222390,"corporation":false,"usgs":false,"family":"Couch","given":"Ann","email":"","affiliations":[{"id":36245,"text":"NPS","active":true,"usgs":false}],"preferred":false,"id":782035,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70227717,"text":"70227717 - 2020 - Spatial sampling bias and model complexity in stream-based species distribution models: A case study of Paddlefish (Polyodon spathula) in the Arkansas River basin, USA","interactions":[],"lastModifiedDate":"2022-01-27T16:55:07.591983","indexId":"70227717","displayToPublicDate":"2019-12-25T10:48:41","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7470,"text":"Ecology & Evolution","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Spatial sampling bias and model complexity in stream-based species distribution models: A case study of Paddlefish (Polyodon spathula) in the Arkansas River basin, USA","title":"Spatial sampling bias and model complexity in stream-based species distribution models: A case study of Paddlefish (Polyodon spathula) in the Arkansas River basin, USA","docAbstract":"Leveraging existing presence records and geospatial datasets, species distribution modeling has been widely applied to informing species conservation and restoration efforts. Maxent is one of the most popular modeling algorithms, yet recent research has demonstrated Maxent models are vulnerable to prediction errors related to spatial sampling bias and model complexity. Despite elevated rates of biodiversity imperilment in stream ecosystems, the application of Maxent models to stream networks has lagged, as has the availability of tools to address potential sources of error and calculate model evaluation metrics when modeling in nonraster environments (such as stream networks). Herein, we use Maxent and customized R code to estimate the potential distribution of paddlefish (Polyodon spathula) at a stream-segment level within the Arkansas River basin, USA, while accounting for potential spatial sampling bias and model complexity. Filtering the presence data appeared to adequately remove an eastward, large-river sampling bias that was evident within the unfiltered presence dataset. In particular, our novel riverscape filter provided a repeatable means of obtaining a relatively even coverage of presence data among watersheds and streams of varying sizes. The greatest differences in estimated distributions were observed among models constructed with default versus AICC-selected parameterization. Although all models had similarly high performance and evaluation metrics, the AICC-selected models were more inclusive of westward-situated and smaller, headwater streams. Overall, our results solidified the importance of accounting for model complexity and spatial sampling bias in SDMs constructed within stream networks and provided a roadmap for future paddlefish restoration efforts in the study area.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.5913","usgsCitation":"Taylor, A., Hafen, T., Holley, C.T., Gonzalez, A., and Long, J.M., 2020, Spatial sampling bias and model complexity in stream-based species distribution models: A case study of Paddlefish (Polyodon spathula) in the Arkansas River basin, USA: Ecology & Evolution, v. 10, no. 2, p. 705-717, https://doi.org/10.1002/ece3.5913.","productDescription":"13 p.","startPage":"705","endPage":"717","ipdsId":"IP-108639","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":458296,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.5913","text":"Publisher Index Page"},{"id":394979,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arkansas, Colorado, Kansas, Missouri, Nebraska, New Mexico, Texas","otherGeospatial":"Arkansas River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.314453125,\n 34.08906131584994\n ],\n [\n -91.845703125,\n 34.08906131584994\n ],\n [\n -91.845703125,\n 39.30029918615029\n ],\n [\n -107.314453125,\n 39.30029918615029\n ],\n [\n -107.314453125,\n 34.08906131584994\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"2","noUsgsAuthors":false,"publicationDate":"2019-12-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Taylor, A. T.","contributorId":264887,"corporation":false,"usgs":false,"family":"Taylor","given":"A. T.","affiliations":[{"id":54572,"text":"University of Central Oklahoma","active":true,"usgs":false}],"preferred":false,"id":831896,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hafen, T.","contributorId":272271,"corporation":false,"usgs":false,"family":"Hafen","given":"T.","email":"","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":831897,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holley, Colt Taylor 0000-0003-4172-4331","orcid":"https://orcid.org/0000-0003-4172-4331","contributorId":272272,"corporation":false,"usgs":true,"family":"Holley","given":"Colt","email":"","middleInitial":"Taylor","affiliations":[{"id":192,"text":"Columbia Environmental Research Center","active":true,"usgs":true}],"preferred":true,"id":831898,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gonzalez, A.","contributorId":272273,"corporation":false,"usgs":false,"family":"Gonzalez","given":"A.","email":"","affiliations":[{"id":7249,"text":"Oklahoma State University","active":true,"usgs":false}],"preferred":false,"id":831899,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Long, James M. 0000-0002-8658-9949 jmlong@usgs.gov","orcid":"https://orcid.org/0000-0002-8658-9949","contributorId":3453,"corporation":false,"usgs":true,"family":"Long","given":"James","email":"jmlong@usgs.gov","middleInitial":"M.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":831900,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70208003,"text":"70208003 - 2020 - Assessing the water quality impacts of two Category-5 hurricanes on St. Thomas, Virgin Islands","interactions":[],"lastModifiedDate":"2020-01-23T09:34:37","indexId":"70208003","displayToPublicDate":"2019-12-24T09:28:07","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3716,"text":"Water Research","onlineIssn":"1879-2448","printIssn":"0043-1354","active":true,"publicationSubtype":{"id":10}},"title":"Assessing the water quality impacts of two Category-5 hurricanes on St. Thomas, Virgin Islands","docAbstract":"Managing waterborne and water-related diseases is one of the most critical factors in the aftermath of hurricane-induced natural disasters. The goal of the study was to identify water-quality impairments in order to set the priorities for post-hurricane relief and to guide future decisions on disaster preparation and relief administration. Field investigations were carried out on St. Thomas, U.S. Virgin Islands as soon as the disaster area became accessible after the back-to-back hurricane strikes by Irma and Maria in 2017. Water samples were collected from individual household rain cisterns, the coastal ocean, and street-surface runoffs for microbial concentration. The microbial community structure and the occurrence of potential human pathogens were investigated in samples using next generation sequencing. Loop mediated isothermal amplification was employed to detect fecal indicator bacteria, Enterococcus faecalis. The results showed both fecal indicator bacteria and Legionella genetic markers were prevalent but were low in concentration in the water samples. Among the 22 cistern samples, 86% were positive for Legionella and 82% for Escherichia-Shigella. Enterococcus faecalis was detected in over 68% of the rain cisterns and in 60% of the coastal waters (n = 20). Microbial community composition in coastal water samples was significantly different from cistern water and runoff water. Although identification at bacterial genus level is not direct evidence of human pathogens, our results suggest cistern water quality needs more organized attention for protection of human health, and that preparation and prevention measures should be taken before natural disasters strike.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.watres.2019.115440","usgsCitation":"Jiang, S., Han, M., Chandrasekaran, S., Fang, Y., and Kellogg, C.A., 2020, Assessing the water quality impacts of two Category-5 hurricanes on St. Thomas, Virgin Islands: Water Research, v. 171, 115440, 9 p., https://doi.org/10.1016/j.watres.2019.115440.","productDescription":"115440, 9 p.","ipdsId":"IP-109410","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":458299,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.watres.2019.115440","text":"Publisher Index Page"},{"id":371493,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"St. Thomas, U.S, Virgin Islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -65.09811401367188,\n 18.24761153423444\n ],\n [\n -64.72457885742188,\n 18.24761153423444\n ],\n [\n -64.72457885742188,\n 18.419684546193967\n ],\n [\n -65.09811401367188,\n 18.419684546193967\n ],\n [\n -65.09811401367188,\n 18.24761153423444\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"171","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jiang, Sunny","contributorId":221746,"corporation":false,"usgs":false,"family":"Jiang","given":"Sunny","email":"","affiliations":[{"id":40412,"text":"University of California, Irvine, CA","active":true,"usgs":false}],"preferred":false,"id":780109,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Han, Muyue","contributorId":221747,"corporation":false,"usgs":false,"family":"Han","given":"Muyue","email":"","affiliations":[{"id":40412,"text":"University of California, Irvine, CA","active":true,"usgs":false}],"preferred":false,"id":780110,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Chandrasekaran, Srikiran","contributorId":221748,"corporation":false,"usgs":false,"family":"Chandrasekaran","given":"Srikiran","email":"","affiliations":[{"id":40412,"text":"University of California, Irvine, CA","active":true,"usgs":false}],"preferred":false,"id":780111,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fang, Yingcong","contributorId":221749,"corporation":false,"usgs":false,"family":"Fang","given":"Yingcong","email":"","affiliations":[{"id":40412,"text":"University of California, Irvine, CA","active":true,"usgs":false}],"preferred":false,"id":780112,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kellogg, Christina A. 0000-0002-6492-9455 ckellogg@usgs.gov","orcid":"https://orcid.org/0000-0002-6492-9455","contributorId":391,"corporation":false,"usgs":true,"family":"Kellogg","given":"Christina","email":"ckellogg@usgs.gov","middleInitial":"A.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true},{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"preferred":true,"id":780108,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70207590,"text":"70207590 - 2020 - Colony-forming unit spreadplate assay versus liquid culture enrichment-polymerase chain reaction assay for the detection of Bacillus Endospores in soils","interactions":[],"lastModifiedDate":"2019-12-30T16:20:46","indexId":"70207590","displayToPublicDate":"2019-12-21T16:19:07","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1816,"text":"Geosciences","active":true,"publicationSubtype":{"id":10}},"title":"Colony-forming unit spreadplate assay versus liquid culture enrichment-polymerase chain reaction assay for the detection of Bacillus Endospores in soils","docAbstract":"A liquid culture enrichment-polymerase chain reaction (E-PCR) assay was investigated as a potential tool to overcome inhibition by chemical component, debris, and background biological impurities in soil that were affecting detection assay performance for soil samples containing Bacillus atrophaeus subsp. globigii (a surrogate for B. anthracis). To evaluate this assay, 9 g of matched sets of three different soil types (loamy sand [sand], sandy loam [loam] and clay) was spiked with 0, ~4.5, 45, 225, 675 and 1350 endospores. One matched set was evaluated using a previously published endospore concentration and colony-forming unit spreadplate (CFU-S) assay and the other matched set was evaluated using an E-PCR assay to investigate differences in limits of detection between the two assays. Data illustrated that detection using the CFU-S assay at the 45-endospore spike level started to become sporadic whereas the E-PCR assay produced repeatable detection at the ~4.5-endospore spike concentration. The E-PCR produced an ~2-log increase in sensitivity and required slightly less time to complete than the CFU-S assay. This study also investigated differences in recovery among pure and blended sand and clay soils and found potential activation of B. anthracis in predominately clay-based soils.","language":"English","publisher":"MDPI","doi":"10.3390/geosciences10010005","usgsCitation":"Griffin, D.W., Lisle, J.T., Feldhake, D., and Silvestri, E.E., 2020, Colony-forming unit spreadplate assay versus liquid culture enrichment-polymerase chain reaction assay for the detection of Bacillus Endospores in soils: Geosciences, v. 1, no. 10, 5, 14 p., https://doi.org/10.3390/geosciences10010005.","productDescription":"5, 14 p.","ipdsId":"IP-105751","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":458313,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/geosciences10010005","text":"Publisher Index Page"},{"id":370875,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"1","issue":"10","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2019-12-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Griffin, Dale W. 0000-0003-1719-5812 dgriffin@usgs.gov","orcid":"https://orcid.org/0000-0003-1719-5812","contributorId":2178,"corporation":false,"usgs":true,"family":"Griffin","given":"Dale","email":"dgriffin@usgs.gov","middleInitial":"W.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":778623,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lisle, John T. 0000-0002-5447-2092 jlisle@usgs.gov","orcid":"https://orcid.org/0000-0002-5447-2092","contributorId":2944,"corporation":false,"usgs":true,"family":"Lisle","given":"John","email":"jlisle@usgs.gov","middleInitial":"T.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":778624,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Feldhake, David","contributorId":176367,"corporation":false,"usgs":false,"family":"Feldhake","given":"David","email":"","affiliations":[],"preferred":false,"id":778625,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Silvestri, Erin E.","contributorId":127343,"corporation":false,"usgs":false,"family":"Silvestri","given":"Erin","email":"","middleInitial":"E.","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":778626,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70211033,"text":"70211033 - 2020 - From theory to experiments for testing the proximate mechanisms of mast seeding: An agenda for an experimental ecology","interactions":[],"lastModifiedDate":"2020-07-10T20:47:51.716539","indexId":"70211033","displayToPublicDate":"2019-12-19T15:44:29","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1466,"text":"Ecology Letters","active":true,"publicationSubtype":{"id":10}},"title":"From theory to experiments for testing the proximate mechanisms of mast seeding: An agenda for an experimental ecology","docAbstract":"Highly variable and synchronised production of seeds by plant populations is called masting and is implicated in many important ecological processes, but how it arises remains poorly understood. The lack of experimental studies prevents underlying mechanisms from being explicitly tested, and thereby precludes meaningful predictions on the consequences of changing environments for plant reproductive patterns and global vegetation dynamics. Here we review the most relevant hypothetical drivers of masting and outline a research agenda that takes the biology of masting from a largely observational field of ecology to one rooted in mechanistic understanding. We divide the experimental framework into three main processes: resource dynamics, pollen limitation, and genetic and hormonal regulation, and illustrate how specific predictions about proximate mechanisms can be tested, highlighting the few successful experiments as examples. We envision that the experiments we outline will deliver new insights into how and why masting patterns might respond to a changing environment.","language":"English","publisher":"Wiley","doi":"10.1111/ele.13442","usgsCitation":"Bogdziewicz, M., Ascoli, D., Hacket-Pain, A., Koenig, W., Pearse, I., Pesendorfer, M.B., Satake, A., Thomas, P., Vacchiano, G., Wohlgemuth, T., and Tanentzap, A., 2020, From theory to experiments for testing the proximate mechanisms of mast seeding: An agenda for an experimental ecology: Ecology Letters, v. 23, no. 2, p. 210-220, https://doi.org/10.1111/ele.13442.","productDescription":"11 p.","startPage":"210","endPage":"220","ipdsId":"IP-113960","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":458328,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/ele.13442","text":"Publisher Index Page"},{"id":376267,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","issue":"2","noUsgsAuthors":false,"publicationDate":"2019-12-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Bogdziewicz, M.","contributorId":228912,"corporation":false,"usgs":false,"family":"Bogdziewicz","given":"M.","affiliations":[{"id":40150,"text":"Adam Mickiewicz University, Poland","active":true,"usgs":false}],"preferred":false,"id":792497,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ascoli, Davide","contributorId":224289,"corporation":false,"usgs":false,"family":"Ascoli","given":"Davide","email":"","affiliations":[{"id":40848,"text":"University of Torino","active":true,"usgs":false}],"preferred":false,"id":792498,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hacket-Pain, Andrew","contributorId":224290,"corporation":false,"usgs":false,"family":"Hacket-Pain","given":"Andrew","affiliations":[{"id":16977,"text":"University of Liverpool","active":true,"usgs":false}],"preferred":false,"id":792499,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Koenig, W. D.","contributorId":225096,"corporation":false,"usgs":false,"family":"Koenig","given":"W. 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The REE-rich terrane occurs in a 2.5-km-wide, northwest-trending belt of Mesoproterozoic (1.4 Ga) stocks and dikes, which intrude a larger Paleoproterozoic (1.7 Ga) metamorphic block that extends ∼10 km southward from Clark Mountain to the eastern Mescal Range. To characterize the REE terrane, gravity, magnetic, magnetotelluric, and whole-rock physical property data were analyzed. Geophysical data reveal that the Mountain Pass carbonatite body is associated with an ∼5 mGal local gravity high that is superimposed on a gravity terrace (∼4 km wide) caused by granitic Paleoproterozoic host rocks. Physical rock property data indicate that the Mountain Pass REE suite is essentially nonmagnetic at the surface with a magnetic susceptibility of 2.0 × 10−3 SI (n = 57), and lower-than-expected magnetizations may be the result of alteration. However, aeromagnetic data indicate that the intrusive suite occurs along the eastern edge of a distinct northwest-trending aeromagnetic high along the eastern Mescal Range. The source of this magnetic anomaly is ∼1.5–2 km below the surface and coincides with an electrical conductivity zone that is several orders of magnitude more conductive than the surrounding rock. The source of the magnetic anomaly is likely a moderately magnetic pluton. Combined geophysical data and models suggest that the carbonatite and its associated REE-enriched ultrapotassic suite were preferentially emplaced along a northwest-trending zone of weakness, which has potential implications for regional mineral exploration.
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