{"pageNumber":"67","pageRowStart":"1650","pageSize":"25","recordCount":4111,"records":[{"id":70102302,"text":"70102302 - 2014 - West Nile Virus transmission in winter: the 2013 Great Salt Lake Bald Eagle and Eared Grebes Mortality event","interactions":[],"lastModifiedDate":"2018-01-17T10:59:38","indexId":"70102302","displayToPublicDate":"2014-04-22T08:56:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2977,"text":"PLOS Current Outbreaks","active":true,"publicationSubtype":{"id":10}},"title":"West Nile Virus transmission in winter: the 2013 Great Salt Lake Bald Eagle and Eared Grebes Mortality event","docAbstract":"

West Nile Virus (WNV) infection has been reported in over 300 species of birds and mammals. Raptors such as eagles, hawks and falcons are remarkably susceptible, but reports of WNV infection in Bald Eagles (Haliaeetus leucocephalus) are rare and reports of WNV infection in grebes (Podicipediformes) even rarer. We report an unusually large wild bird mortality event involving between 15,000-20,000 Eared Grebes (Podiceps nigricollis) and over 40 Bald Eagles around the Great Salt Lake, Utah, in November-December 2013. Mortality in grebes was first reported in early November during a period when the area was unseasonably warm and the grebes were beginning to gather and stage prior to migration. Ten out of ten Eared Grebes collected during this period were WNV RT-PCR and/or isolation positive. This is the first report of WNV infection in Eared Grebes and the associated mortality event is matched in scale only by the combined outbreaks in American White Pelican (Pelecanus erythrorhynchos) colonies in the north central states in 2002-2003. We cannot be sure that all of the grebes were infected by mosquito transmission; some may have become infected through contact with WNV shed orally or cloacally from other infected grebes. Beginning in early December, Bald Eagles in the Great Salt Lake area were observed to display neurological signs such as body tremors, limb paralysis and lethargy. At least 43 Bald Eagles had died by the end of the month. Nine of nine Bald Eagles examined were infected with WNV. To the best of our knowledge, this is the largest single raptor mortality event since WNV became endemic in the USA. Because the majority of the eagles affected were found after onset of below-freezing temperatures, we suggest at least some of the Bald Eagles were infected with WNV via consumption of infected Eared Grebes or horizontal transmission at roost sites.

","language":"English","publisher":"PLoS","doi":"10.1371/currents.outbreaks.b0f031fc8db2a827d9da0f30f0766871","usgsCitation":"Ip, S., Van Wettere, A.J., McFarlan, L., Shearn-Bochsler, V.I., Dickson, S.L., Baker, J., Hatch, G., Cavender, K., Long, R.R., and Bodenstein, B.L., 2014, West Nile Virus transmission in winter: the 2013 Great Salt Lake Bald Eagle and Eared Grebes Mortality event: PLOS Current Outbreaks, 12 p., https://doi.org/10.1371/currents.outbreaks.b0f031fc8db2a827d9da0f30f0766871.","productDescription":"12 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-054081","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":473046,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/3994192","text":"Publisher Index Page"},{"id":286478,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":286477,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1371/currents.outbreaks.b0f031fc8db2a827d9da0f30f0766871"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5357815ae4b0938066bc81ab","contributors":{"authors":[{"text":"Ip, S. 0000-0003-4844-7533 hip@usgs.gov","orcid":"https://orcid.org/0000-0003-4844-7533","contributorId":727,"corporation":false,"usgs":true,"family":"Ip","given":"S.","email":"hip@usgs.gov","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":492918,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Wettere, Arnaud J.","contributorId":63317,"corporation":false,"usgs":true,"family":"Van Wettere","given":"Arnaud","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":492925,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McFarlan, Leslie","contributorId":71482,"corporation":false,"usgs":true,"family":"McFarlan","given":"Leslie","email":"","affiliations":[],"preferred":false,"id":492926,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shearn-Bochsler, Valerie I. 0000-0002-5590-6518 vbochsler@usgs.gov","orcid":"https://orcid.org/0000-0002-5590-6518","contributorId":3234,"corporation":false,"usgs":true,"family":"Shearn-Bochsler","given":"Valerie","email":"vbochsler@usgs.gov","middleInitial":"I.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":492919,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Dickson, Sammie L.","contributorId":107617,"corporation":false,"usgs":true,"family":"Dickson","given":"Sammie","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":492927,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Baker, JoDee","contributorId":60956,"corporation":false,"usgs":true,"family":"Baker","given":"JoDee","email":"","affiliations":[],"preferred":false,"id":492924,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hatch, Gary","contributorId":42877,"corporation":false,"usgs":true,"family":"Hatch","given":"Gary","email":"","affiliations":[],"preferred":false,"id":492923,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cavender, Kimberly","contributorId":23449,"corporation":false,"usgs":true,"family":"Cavender","given":"Kimberly","email":"","affiliations":[],"preferred":false,"id":492922,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Long, Renee Romaine rlong@usgs.gov","contributorId":3826,"corporation":false,"usgs":true,"family":"Long","given":"Renee","email":"rlong@usgs.gov","middleInitial":"Romaine","affiliations":[],"preferred":true,"id":492920,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Bodenstein, Barbara L. 0000-0001-7946-0103 bbodenstein@usgs.gov","orcid":"https://orcid.org/0000-0001-7946-0103","contributorId":4389,"corporation":false,"usgs":true,"family":"Bodenstein","given":"Barbara","email":"bbodenstein@usgs.gov","middleInitial":"L.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":492921,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70095728,"text":"tm7C11 - 2014 - fatalityCMR: capture-recapture software to correct raw counts of wildlife fatalities using trial experiments for carcass detection probability and persistence time","interactions":[],"lastModifiedDate":"2024-03-04T20:03:47.379694","indexId":"tm7C11","displayToPublicDate":"2014-04-17T13:44:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":335,"text":"Techniques and Methods","code":"TM","onlineIssn":"2328-7055","printIssn":"2328-7047","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"7-C11","title":"fatalityCMR: capture-recapture software to correct raw counts of wildlife fatalities using trial experiments for carcass detection probability and persistence time","docAbstract":"Many industrial and agricultural activities involve wildlife fatalities by collision, poisoning or other involuntary harvest: wind turbines, highway network, utility network, tall structures, pesticides, etc. Impacted wildlife may benefit from official protection, including the requirement to monitor the impact. Carcass counts can often be conducted to quantify the number of fatalities, but they need to be corrected for carcass persistence time (removal by scavengers and decay) and detection probability (searcher efficiency). In this article we introduce a new piece of software that fits a superpopulation capture-recapture model to raw count data. It uses trial data to estimate detection and daily persistence probabilities. A recurrent issue is that fatalities of rare, protected species are infrequent, in which case the software offers the option to switch to an ‘evidence of absence’ mode, i.e., estimate the number of carcasses that may have been missed by field crews. The software allows distinguishing between different turbine types (e.g. different vegetation cover under turbines, or different technical properties), as well between two carcass age-classes or states, with transition between those classes (e.g, fresh and dry). There is a data simulation capacity that may be used at the planning stage to optimize sampling design. Resulting mortality estimates can be used 1) to quantify the required amount of compensation, 2) inform mortality projections for proposed development sites, and 3) inform decisions about management of existing sites.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm7C11","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Peron, G., and Hines, J., 2014, fatalityCMR: capture-recapture software to correct raw counts of wildlife fatalities using trial experiments for carcass detection probability and persistence time: U.S. Geological Survey Techniques and Methods 7-C11, iv, 14 p., https://doi.org/10.3133/tm7C11.","productDescription":"iv, 14 p.","onlineOnly":"Y","ipdsId":"IP-050478","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":286402,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/tm7c11.jpg"},{"id":286400,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/tm/07/c11/"},{"id":286401,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/07/c11/pdf/tm7-c11.pdf"}],"country":"United States","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -124.8,24.5 ], [ -124.8,49.383333 ], [ -66.95,49.383333 ], [ -66.95,24.5 ], [ -124.8,24.5 ] ] ] } } ] }","contact":"

Contact Pubs Warehouse

","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5350e9d5e4b05569d805573b","contributors":{"authors":[{"text":"Peron, Guillaume","contributorId":64569,"corporation":false,"usgs":true,"family":"Peron","given":"Guillaume","email":"","affiliations":[],"preferred":false,"id":491411,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hines, James E. jhines@usgs.gov","contributorId":3506,"corporation":false,"usgs":true,"family":"Hines","given":"James E.","email":"jhines@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":491410,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70147861,"text":"70147861 - 2014 - Estimating abundances of interacting species using morphological traits, foraging guilds, and habitat","interactions":[],"lastModifiedDate":"2015-05-11T13:18:03","indexId":"70147861","displayToPublicDate":"2014-04-11T14:15:00","publicationYear":"2014","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":"Estimating abundances of interacting species using morphological traits, foraging guilds, and habitat","docAbstract":"

We developed a statistical model to estimate the abundances of potentially interacting species encountered while conducting point-count surveys at a set of ecologically relevant locations - as in a metacommunity of species. In the model we assume that abundances of species with similar traits (e.g., body size) are potentially correlated and that these correlations, when present, may exist among all species or only among functionally related species (such as members of the same foraging guild). We also assume that species-specific abundances vary among locations owing to systematic and stochastic sources of heterogeneity. For example, if abundances differ among locations due to differences in habitat, then measures of habitat may be included in the model as covariates. Naturally, the quantitative effects of these covariates are assumed to differ among species. Our model also accounts for the effects of detectability on the observed counts of each species. This aspect of the model is especially important for rare or uncommon species that may be difficult to detect in community-level surveys. Estimating the detectability of each species requires sampling locations to be surveyed repeatedly using different observers or different visits of a single observer. As an illustration, we fitted models to species-specific counts of birds obtained while sampling an avian community during the breeding season. In the analysis we examined whether species abundances appeared to be correlated due to similarities in morphological measures (body mass, beak length, tarsus length, wing length, tail length) and whether these correlations existed among all species or only among species of the same foraging guild. We also used the model to estimate the effects of forested area on species abundances and the effects of sound power output (as measured by body size) on species detection probabilities.

","language":"English","publisher":"Public Library of Science","publisherLocation":"San Francisco, CA","doi":"10.1371/journal.pone.0094323","usgsCitation":"Dorazio, R., and Connor, E., 2014, Estimating abundances of interacting species using morphological traits, foraging guilds, and habitat: PLoS ONE, v. 9, no. 4, p. 1-9, https://doi.org/10.1371/journal.pone.0094323.","productDescription":"9 p.","startPage":"1","endPage":"9","numberOfPages":"9","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-045166","costCenters":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"links":[{"id":473055,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0094323","text":"Publisher Index Page"},{"id":300309,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","issue":"4","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2014-04-11","publicationStatus":"PW","scienceBaseUri":"5551d2b2e4b0a92fa7e93bdf","contributors":{"authors":[{"text":"Dorazio, Robert M. bob_dorazio@usgs.gov","contributorId":140635,"corporation":false,"usgs":true,"family":"Dorazio","given":"Robert M.","email":"bob_dorazio@usgs.gov","affiliations":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true}],"preferred":false,"id":546346,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Connor, Edward F.","contributorId":17503,"corporation":false,"usgs":true,"family":"Connor","given":"Edward F.","affiliations":[],"preferred":false,"id":546347,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70101024,"text":"70101024 - 2014 - Blood lead concentrations in Alaskan tundra swans: linking breeding and wintering areas with satellite telemetry","interactions":[],"lastModifiedDate":"2018-09-14T15:53:03","indexId":"70101024","displayToPublicDate":"2014-04-07T10:55:47","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1479,"text":"Ecotoxicology","active":true,"publicationSubtype":{"id":10}},"title":"Blood lead concentrations in Alaskan tundra swans: linking breeding and wintering areas with satellite telemetry","docAbstract":"Tundra swans (Cygnus columbianus) like many waterfowl species are susceptible to lead (Pb) poisoning, and Pb-induced mortality has been reported from many areas of their wintering range. Little is known however about Pb levels throughout the annual cycle of tundra swans, especially during summer when birds are on remote northern breeding areas where they are less likely to be exposed to anthropogenic sources of Pb. Our objective was to document summer Pb levels in tundra swans throughout their breeding range in Alaska to determine if there were population-specific differences in blood Pb concentrations that might pose a threat to swans and to humans that may consume them. We measured blood Pb concentrations in tundra swans at five locations in Alaska, representing birds that winter in both the Pacific Flyway and Atlantic Flyway. We also marked swans at each location with satellite transmitters and coded neck bands, to identify staging and wintering sites and determine if winter site use correlated with summer Pb concentrations. Blood Pb levels were generally low ( < 0.2 μg/ml) in swans across all breeding areas. Pb levels were lower in cygnets than adults, suggesting that swans were likely exposed to Pb on wintering areas or on return migration to Alaska, rather than on the summer breeding grounds. Blood Pb levels varied significantly across the five breeding areas, with highest concentrations in birds on the North Slope of Alaska (wintering in the Atlantic Flyway), and lowest in birds from the lower Alaska Peninsula that rarely migrate south for winter.","language":"English","publisher":"Springer","doi":"10.1007/s10646-014-1192-z","usgsCitation":"Ely, C.R., and Franson, C., 2014, Blood lead concentrations in Alaskan tundra swans: linking breeding and wintering areas with satellite telemetry: Ecotoxicology, v. 23, no. 3, p. 349-356, https://doi.org/10.1007/s10646-014-1192-z.","productDescription":"8 p.","startPage":"349","endPage":"356","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-053240","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true},{"id":34983,"text":"Contaminant Biology Program","active":true,"usgs":true}],"links":[{"id":285950,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":285949,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1007/s10646-014-1192-z"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -141.240234375,\n 69.7181066990676\n ],\n [\n -156.09375,\n 71.41317683396566\n ],\n [\n -166.55273437499997,\n 68.75231494434473\n ],\n [\n -168.57421875,\n 65.47650756256367\n ],\n [\n -165.41015625,\n 59.62332522313024\n ],\n [\n -159.345703125,\n 57.562995459387146\n ],\n [\n -167.16796875,\n 54.36775852406841\n ],\n [\n -177.890625,\n 52.482780222078205\n ],\n [\n -187.3828125,\n 53.54030739150022\n ],\n [\n -187.998046875,\n 52.429222277955134\n ],\n [\n -177.275390625,\n 51.01375465718821\n ],\n [\n -166.904296875,\n 52.802761415419674\n ],\n [\n -161.279296875,\n 54.77534585936447\n ],\n [\n -151.611328125,\n 56.84897198026975\n ],\n [\n -150.99609375,\n 58.768200159239576\n ],\n [\n -146.42578125,\n 59.84481485969105\n ],\n [\n -140.9765625,\n 59.57885104663186\n ],\n [\n -141.240234375,\n 69.7181066990676\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"23","issue":"3","noUsgsAuthors":false,"publicationDate":"2014-01-28","publicationStatus":"PW","scienceBaseUri":"53517029e4b05569d805a17b","contributors":{"authors":[{"text":"Ely, Craig R. 0000-0003-4262-0892 cely@usgs.gov","orcid":"https://orcid.org/0000-0003-4262-0892","contributorId":3214,"corporation":false,"usgs":true,"family":"Ely","given":"Craig","email":"cely@usgs.gov","middleInitial":"R.","affiliations":[{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":492546,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Franson, Christian 0000-0002-0251-4238","orcid":"https://orcid.org/0000-0002-0251-4238","contributorId":58941,"corporation":false,"usgs":true,"family":"Franson","given":"Christian","affiliations":[],"preferred":false,"id":492547,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70100991,"text":"70100991 - 2014 - Parasite-mediated selection drives an immunogenetic tradeoff in plains zebra (Equus quagga)","interactions":[],"lastModifiedDate":"2014-04-09T10:02:29","indexId":"70100991","displayToPublicDate":"2014-04-06T09:58:09","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3174,"text":"Proceedings of the Royal Society B: Biological Sciences","active":true,"publicationSubtype":{"id":10}},"title":"Parasite-mediated selection drives an immunogenetic tradeoff in plains zebra (Equus quagga)","docAbstract":"Pathogen evasion of the host immune system is a key force driving extreme polymorphism in genes of the major histocompatibility complex (MHC). Although this gene family is well characterized in structure and function, there is still much debate surrounding the mechanisms by which MHC diversity is selectively maintained. Many studies have investigated relationships between MHC variation and specific pathogens, and have found mixed support for and against the hypotheses of heterozygote advantage, frequency-dependent or fluctuating selection. Few, however, have focused on the selective effects of multiple parasite types on host immunogenetic patterns. Here, we examined relationships between variation in the equine MHC gene, ELA-DRA, and both gastrointestinal (GI) and ectoparasitism in plains zebras (Equus quagga). Specific alleles present at opposing population frequencies had antagonistic effects, with rare alleles associated with increased GI parasitism and common alleles with increased tick burdens. These results support a frequency-dependent mechanism, but are also consistent with fluctuating selection. Maladaptive GI parasite ‘susceptibility alleles’ were reduced in frequency, suggesting that these parasites may play a greater selective role at this locus. Heterozygote advantage, in terms of allele mutational divergence, also predicted decreased GI parasite burden in genotypes with a common allele. We conclude that an immunogenetic trade-off affects resistance/susceptibility to parasites in this system. Because GI and ectoparasites do not directly interact within hosts, our results uniquely show that antagonistic parasite interactions can be indirectly modulated through the host immune system. This study highlights the importance of investigating the role of multiple parasites in shaping patterns of host immunogenetic variation.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Proceedings of the Royal Society B: Biological Sciences","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Royal Society Publishing","doi":"10.1098/rspb.2014.0077","usgsCitation":"Kamath, P.L., Turner, W., Kusters, M., and Getz, W.M., 2014, Parasite-mediated selection drives an immunogenetic tradeoff in plains zebra (Equus quagga): Proceedings of the Royal Society B: Biological Sciences, v. 281, no. 1783, https://doi.org/10.1098/rspb.2014.0077.","ipdsId":"IP-050719","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":473065,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rspb.2014.0077","text":"Publisher Index Page"},{"id":285938,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":285937,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1098/rspb.2014.0077"}],"volume":"281","issue":"1783","noUsgsAuthors":false,"publicationDate":"2014-05-22","publicationStatus":"PW","scienceBaseUri":"53517059e4b05569d805a359","contributors":{"authors":[{"text":"Kamath, Pauline L. pkamath@usgs.gov","contributorId":4517,"corporation":false,"usgs":true,"family":"Kamath","given":"Pauline","email":"pkamath@usgs.gov","middleInitial":"L.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":492491,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Turner, Wendy C.","contributorId":36458,"corporation":false,"usgs":true,"family":"Turner","given":"Wendy C.","affiliations":[],"preferred":false,"id":492492,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kusters, Martina","contributorId":91785,"corporation":false,"usgs":true,"family":"Kusters","given":"Martina","email":"","affiliations":[],"preferred":false,"id":492494,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Getz, Wayne M.","contributorId":64563,"corporation":false,"usgs":true,"family":"Getz","given":"Wayne","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":492493,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70048943,"text":"ds795 - 2014 - Groundwater-quality data in seven GAMA study units: results from initial sampling, 2004-2005, and resampling, 2007-2008, of wells: California GAMA Program Priority Basin Project","interactions":[],"lastModifiedDate":"2026-05-20T19:18:54.504554","indexId":"ds795","displayToPublicDate":"2014-04-03T16:06:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"795","title":"Groundwater-quality data in seven GAMA study units: results from initial sampling, 2004-2005, and resampling, 2007-2008, of wells: California GAMA Program Priority Basin Project","docAbstract":"

The Priority Basin Project (PBP) of the Groundwater Ambient Monitoring and Assessment (GAMA) Program was developed in response to the Groundwater Quality Monitoring Act of 2001 and is being conducted by the U.S. Geological Survey (USGS) in cooperation with the California State Water Resources Control Board (SWRCB). The GAMA-PBP began sampling, primarily public supply wells in May 2004. By the end of February 2006, seven (of what would eventually be 35) study units had been sampled over a wide area of the State. Selected wells in these first seven study units were resampled for water quality from August 2007 to November 2008 as part of an assessment of temporal trends in water quality by the GAMA-PBP.

\n
\n

The initial sampling was designed to provide a spatially unbiased assessment of the quality of raw groundwater used for public water supplies within the seven study units. In the 7 study units, 462 wells were selected by using a spatially distributed, randomized grid-based method to provide statistical representation of the study area. Wells selected this way are referred to as grid wells or status wells. Approximately 3 years after the initial sampling, 55 of these previously sampled status wells (approximately 10 percent in each study unit) were randomly selected for resampling. The seven resampled study units, the total number of status wells sampled for each study unit, and the number of these wells resampled for trends are as follows, in chronological order of sampling: San Diego Drainages (53 status wells, 7 trend wells), North San Francisco Bay (84, 10), Northern San Joaquin Basin (51, 5), Southern Sacramento Valley (67, 7), San Fernando–San Gabriel (35, 6), Monterey Bay and Salinas Valley Basins (91, 11), and Southeast San Joaquin Valley (83, 9).

\n
\n

The groundwater samples were analyzed for a large number of synthetic organic constituents (volatile organic compounds [VOCs], pesticides, and pesticide degradates), constituents of special interest (perchlorate, N-nitrosodimethylamine [NDMA], and 1,2,3-trichloropropane [1,2,3-TCP]), and naturally-occurring inorganic constituents (nutrients, major and minor ions, and trace elements). Naturally-occurring isotopes (tritium, carbon-14, and stable isotopes of hydrogen and oxygen in water) also were measured to help identify processes affecting groundwater quality and the sources and ages of the sampled groundwater. Nearly 300 constituents and water-quality indicators were investigated.

\n
\n

Quality-control samples (blanks, replicates, and samples for matrix spikes) were collected at 24 percent of the 55 status wells resampled for trends, and the results for these samples were used to evaluate the quality of the data for the groundwater samples. Field blanks rarely contained detectable concentrations of any constituent, suggesting that contamination was not a noticeable source of bias in the data for the groundwater samples. Differences between replicate samples were mostly within acceptable ranges, indicating acceptably low variability in analytical results. Matrix-spike recoveries were within the acceptable range (70 to 130 percent) for 75 percent of the compounds for which matrix spikes were collected.

\n
\n

This study did not attempt to evaluate the quality of water delivered to consumers. After withdrawal, groundwater typically is treated, disinfected, and blended with other waters to maintain acceptable water quality. The benchmarks used in this report apply to treated water that is served to the consumer, not to untreated groundwater. To provide some context for the results, however, concentrations of constituents measured in these groundwater samples were compared with benchmarks established by the U.S. Environmental Protection Agency (USEPA) and California Department of Public Health (CDPH). Comparisons between data collected for this study and benchmarks for drinking water are for illustrative purposes only and are not indicative of compliance or non-compliance with those benchmarks.

\n
\n

Most constituents that were detected in groundwater samples from the trend wells were found at concentrations less than drinking-water benchmarks. Four VOCs—trichloroethene, tetrachloroethene, 1,2-dibromo-3-chloropropane, and methyl tert-butyl ether—were detected in one or more wells at concentrations greater than their health-based benchmarks, and six VOCs were detected in at least 10 percent of the samples during initial sampling or resampling of the trend wells. No pesticides were detected at concentrations near or greater than their health-based benchmarks. Three pesticide constituents—atrazine, deethylatrazine, and simazine—were detected in more than 10 percent of the trend-well samples during both sampling periods. Perchlorate, a constituent of special interest, was detected more frequently, and at greater concentrations during resampling than during initial sampling, but this may be due to a change in analytical method between the sampling periods, rather than to a change in groundwater quality. Another constituent of special interest, 1,2,3-TCP, was also detected more frequently during resampling than during initial sampling, but this pattern also may not reflect a change in groundwater quality. Samples from several of the wells where 1,2,3-TCP was detected by low-concentration-level analysis during resampling were not analyzed for 1,2,3-TCP using a low-level method during initial sampling. Most detections of nutrients and trace elements in samples from trend wells were less than health-based benchmarks during both sampling periods. Exceptions include nitrate, arsenic, boron, and vanadium, all detected at concentrations greater than their health-based benchmarks in at least one well during both sampling periods, and molybdenum, detected at concentrations greater than its health-based benchmark during resampling only. The isotopic ratios of oxygen and hydrogen in water and tritium and carbon-14 activities generally changed little between sampling periods, suggesting that the predominant sources and ages of groundwater in most trend wells were consistent between the sampling periods.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds795","collaboration":"A product of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program. Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Kent, R.H., Belitz, K., and Fram, M.S., 2014, Groundwater-quality data in seven GAMA study units: results from initial sampling, 2004-2005, and resampling, 2007-2008, of wells: California GAMA Program Priority Basin Project: U.S. Geological Survey Data Series 795, x, 170 p., https://doi.org/10.3133/ds795.","productDescription":"x, 170 p.","numberOfPages":"184","onlineOnly":"Y","ipdsId":"IP-032958","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":504584,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_99880.htm","linkFileType":{"id":5,"text":"html"}},{"id":285664,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/795/pdf/ds795.pdf"},{"id":285663,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/795/"},{"id":285665,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds795.jpg"}],"projection":"Albers Equal Area Conic Projection","country":"United States","state":"California","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -125.0,32.0 ], [ -125.0,42.2 ], [ -114.0,42.2 ], [ -114.0,32.0 ], [ -125.0,32.0 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53517044e4b05569d805a243","contributors":{"authors":[{"text":"Kent, Robert H. 0000-0003-4174-9467 rhkent@usgs.gov","orcid":"https://orcid.org/0000-0003-4174-9467","contributorId":175257,"corporation":false,"usgs":true,"family":"Kent","given":"Robert","email":"rhkent@usgs.gov","middleInitial":"H.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":485827,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Belitz, Kenneth 0000-0003-4481-2345 kbelitz@usgs.gov","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":442,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","email":"kbelitz@usgs.gov","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":485825,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fram, Miranda S. 0000-0002-6337-059X mfram@usgs.gov","orcid":"https://orcid.org/0000-0002-6337-059X","contributorId":1156,"corporation":false,"usgs":true,"family":"Fram","given":"Miranda","email":"mfram@usgs.gov","middleInitial":"S.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":485826,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70148120,"text":"70148120 - 2014 - Guidelines for a priori grouping of species in hierarchical community models","interactions":[],"lastModifiedDate":"2015-06-03T10:40:16","indexId":"70148120","displayToPublicDate":"2014-04-01T11:45:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Guidelines for a priori grouping of species in hierarchical community models","docAbstract":"

Recent methodological advances permit the estimation of species richness and occurrences for rare species by linking species-level occurrence models at the community level. The value of such methods is underscored by the ability to examine the influence of landscape heterogeneity on species assemblages at large spatial scales. A salient advantage of community-level approaches is that parameter estimates for data-poor species are more precise as the estimation process borrows from data-rich species. However, this analytical benefit raises a question about the degree to which inferences are dependent on the implicit assumption of relatedness among species. Here, we assess the sensitivity of community/group-level metrics, and individual-level species inferences given various classification schemes for grouping species assemblages using multispecies occurrence models. We explore the implications of these groupings on parameter estimates for avian communities in two ecosystems: tropical forests in Puerto Rico and temperate forests in northeastern United States. We report on the classification performance and extent of variability in occurrence probabilities and species richness estimates that can be observed depending on the classification scheme used. We found estimates of species richness to be most precise and to have the best predictive performance when all of the data were grouped at a single community level. Community/group-level parameters appear to be heavily influenced by the grouping criteria, but were not driven strictly by total number of detections for species. We found different grouping schemes can provide an opportunity to identify unique assemblage responses that would not have been found if all of the species were analyzed together. We suggest three guidelines: (1) classification schemes should be determined based on study objectives; (2) model selection should be used to quantitatively compare different classification approaches; and (3) sensitivity of results to different classification approaches should be assessed. These guidelines should help researchers apply hierarchical community models in the most effective manner.

","language":"English","publisher":"Blackwell Pub. Ltd.","publisherLocation":"Oxford","doi":"10.1002/ece3.976","usgsCitation":"Pacifici, K., Zipkin, E., Collazo, J., Irizarry, J.I., and DeWan, A.A., 2014, Guidelines for a priori grouping of species in hierarchical community models: Ecology and Evolution, v. 4, no. 7, p. 877-888, https://doi.org/10.1002/ece3.976.","productDescription":"12 p.","startPage":"877","endPage":"888","numberOfPages":"12","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-049249","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":473073,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ece3.976","text":"External Repository"},{"id":301009,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","issue":"7","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2014-02-22","publicationStatus":"PW","scienceBaseUri":"55702539e4b0d9246a9fd1a0","contributors":{"authors":[{"text":"Pacifici, Krishna","contributorId":26564,"corporation":false,"usgs":false,"family":"Pacifici","given":"Krishna","email":"","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":548136,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zipkin, Elise ezipkin@usgs.gov","contributorId":470,"corporation":false,"usgs":true,"family":"Zipkin","given":"Elise","email":"ezipkin@usgs.gov","affiliations":[],"preferred":true,"id":548139,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Collazo, Jaime jaime_collazo@usgs.gov","contributorId":2613,"corporation":false,"usgs":true,"family":"Collazo","given":"Jaime","email":"jaime_collazo@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":547445,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Irizarry, Julissa I.","contributorId":141056,"corporation":false,"usgs":false,"family":"Irizarry","given":"Julissa","email":"","middleInitial":"I.","affiliations":[],"preferred":false,"id":548140,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"DeWan, Amielle A.","contributorId":24486,"corporation":false,"usgs":true,"family":"DeWan","given":"Amielle","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":548141,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70148398,"text":"70148398 - 2014 - Animal reintroductions: an innovative assessment of survival","interactions":[],"lastModifiedDate":"2015-06-02T10:01:23","indexId":"70148398","displayToPublicDate":"2014-04-01T11:00:00","publicationYear":"2014","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":"Animal reintroductions: an innovative assessment of survival","docAbstract":"

Quantitative evaluations of reintroductions are infrequent and assessments of milestones reached before a project is completed, or abandoned due to lack of funding, are rare. However, such assessments, which are promoted in adaptive management frameworks, are critical. Quantification can provide defensible estimates of biological success, such as the number of survivors from a released cohort, with associated cost per animal. It is unlikely that the global issues of endangered wildlife and population declines will abate, therefore, assurance colonies and reintroductions are likely to become more common. If such endeavors are to be successful biologically or achieve adequate funding, implementation must be more rigorous and accountable. We use a novel application of a multistate, robust design capture-recapture model to estimate survival of reintroduced tadpoles through metamorphosis (i.e., the number of individuals emerging from the pond) and thereby provide a quantitative measure of effort and success for an \"in progress\" reintroduction of toads. Our data also suggest that tadpoles released at later developmental stages have an increased probability of survival and that eggs laid in the wild hatched at higher rates than eggs laid by captive toads. We illustrate how an interim assessment can identify problems, highlight successes, and provide information for use in adjusting the effort or implementing a Decision-Theoretic adaptive management strategy.

","language":"English","publisher":"Elsevier Science Ltd.","publisherLocation":"Kidlington, Oxford","doi":"10.1016/j.biocon.2014.02.034","usgsCitation":"Muths, E.L., Bailey, L., and Watry, M.K., 2014, Animal reintroductions: an innovative assessment of survival: Biological Conservation, v. 172, p. 200-208, https://doi.org/10.1016/j.biocon.2014.02.034.","productDescription":"9 p.","startPage":"200","endPage":"208","numberOfPages":"9","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-052357","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":300968,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"172","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"556ed3b5e4b0d9246a9fa7c0","contributors":{"authors":[{"text":"Muths, Erin L. 0000-0002-5498-3132 muthse@usgs.gov","orcid":"https://orcid.org/0000-0002-5498-3132","contributorId":1260,"corporation":false,"usgs":true,"family":"Muths","given":"Erin","email":"muthse@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":547990,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bailey, Larissa L.","contributorId":93183,"corporation":false,"usgs":true,"family":"Bailey","given":"Larissa L.","affiliations":[],"preferred":false,"id":547991,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Watry, Mary Kay","contributorId":141021,"corporation":false,"usgs":false,"family":"Watry","given":"Mary","email":"","middleInitial":"Kay","affiliations":[{"id":7237,"text":"NPS, Olympic National Park","active":true,"usgs":false}],"preferred":false,"id":547992,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70126220,"text":"70126220 - 2014 - Ghost of habitat past: historic habitat affects the contemporary distribution of giant garter snakes in a modified landscape.","interactions":[],"lastModifiedDate":"2014-09-23T10:09:45","indexId":"70126220","displayToPublicDate":"2014-04-01T10:08:03","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":774,"text":"Animal Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Ghost of habitat past: historic habitat affects the contemporary distribution of giant garter snakes in a modified landscape.","docAbstract":"Historic habitat conditions can affect contemporary communities and populations, but most studies of historic habitat are based on the reduction in habitat extent or connectivity. Little is known about the effects of historic habitat on contemporary species distributions when historic habitat has been nearly completely removed, but species persist in a highly altered landscape. More than 93% of the historic wetlands in the Central Valley of California, USA, have been drained and converted to agricultural and other uses, but agricultural wetlands, such as rice and its supporting infrastructure of canals, allow some species to persist. Little is known about the distribution of giant garter snakes Thamnophis gigas, a rare aquatic snake species inhabiting this predominantly agricultural landscape, or the variables that affect where this species occurs. We used occupancy modeling to examine the distribution of giant garter snakes at the landscape scale in the Sacramento Valley (northern portion of the Central Valley) of California, with an emphasis on the relative strength of historic and contemporary variables (landscape-scale habitat, local microhabitat, vegetation composition and relative prey counts) for predicting giant garter snake occurrence. Proximity to historic marsh best explained variation in the probability of occurrence of giant garter snakes at the landscape scale, with greater probability of occurrence near historic marsh. We suspect that the importance of distance to historic marsh represents dispersal limitations of giant garter snakes. These results suggest that preserving and restoring areas near historic marsh, and minimizing activities that reduce the extent of marsh or marsh-like (e.g. rice agriculture, canal) habitats near historic marsh may be advantageous to giant garter snakes.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Animal Conservation","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Cambridge University Press","publisherLocation":"Cambridge, England","doi":"10.1111/acv.12073","usgsCitation":"Halstead, B., Wylie, G.D., and Casazza, M.L., 2014, Ghost of habitat past: historic habitat affects the contemporary distribution of giant garter snakes in a modified landscape.: Animal Conservation, v. 17, no. 2, p. 144-153, https://doi.org/10.1111/acv.12073.","productDescription":"10 p.","startPage":"144","endPage":"153","numberOfPages":"10","ipdsId":"IP-045138","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":294297,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":294232,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1111/acv.12073"}],"volume":"17","issue":"2","noUsgsAuthors":false,"publicationDate":"2013-08-22","publicationStatus":"PW","scienceBaseUri":"5422bb26e4b08312ac7cf044","contributors":{"authors":[{"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":501957,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wylie, Glenn D. 0000-0002-7061-6658 glenn_wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7061-6658","contributorId":3052,"corporation":false,"usgs":true,"family":"Wylie","given":"Glenn","email":"glenn_wylie@usgs.gov","middleInitial":"D.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":501958,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":501956,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70048954,"text":"ds803 - 2014 - Groundwater-quality data in the Klamath Mountains study unit, 2010: results from the California GAMA Program","interactions":[],"lastModifiedDate":"2026-05-28T21:12:12.342599","indexId":"ds803","displayToPublicDate":"2014-03-28T15:19:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"803","title":"Groundwater-quality data in the Klamath Mountains study unit, 2010: results from the California GAMA Program","docAbstract":"

Groundwater quality in the 8,806-square-mile Klamath Mountains (KLAM) study unit was investigated by the U.S. Geological Survey (USGS) from October to December 2010, as part of the California State Water Resources Control Board (SWRCB) Groundwater Ambient Monitoring and Assessment (GAMA) Program’s Priority Basin Project (PBP). The GAMA-PBP was developed in response to the California Groundwater Quality Monitoring Act of 2001 and is being conducted in collaboration with the SWRCB and Lawrence Livermore National Laboratory (LLNL). The KLAM study unit was the thirty-third study unit to be sampled as part of the GAMA-PBP.

\n\n
\n\n

The GAMA Klamath Mountains study was designed to provide a spatially unbiased assessment of untreated-groundwater quality in the primary aquifer system and to facilitate statistically consistent comparisons of untreated-groundwater quality throughout California. The primary aquifer system is defined by the perforation intervals of wells listed in the California Department of Public Health (CDPH) database for the KLAM study unit. Groundwater quality in the primary aquifer system may differ from the quality in the shallower or deeper water-bearing zones; shallower groundwater may be more vulnerable to surficial contamination.

\n\n
\n\n

In the KLAM study unit, groundwater samples were collected from sites in Del Norte, Siskiyou, Humboldt, Trinity, Tehama, and Shasta Counties, California. Of the 39 sites sampled, 38 were selected by using a spatially distributed, randomized grid-based method to provide statistical representation of the primary aquifer system in the study unit (grid sites), and the remaining site was non-randomized (understanding site).

\n\n
\n\n

The groundwater samples were analyzed for basic field parameters, organic constituents (volatile organic compounds [VOCs] and pesticides and pesticide degradates), inorganic constituents (trace elements, nutrients, major and minor ions, total dissolved solids [TDS]), radon-222, gross alpha and gross beta radioactivity, and microbial indicators (total coliform and Escherichia coli [E. coli]). Isotopic tracers (stable isotopes of hydrogen and oxygen in water, isotopic ratios of dissolved strontium in water, and stable isotopes of carbon in dissolved inorganic carbon), dissolved noble gases, and age-dating tracers (tritium and carbon-14) were measured to help identify sources and ages of sampled groundwater.

\n\n
\n\n

Quality-control samples (field blanks, replicate sample pairs, and matrix spikes) were collected at 13 percent of the sites in the KLAM study unit, and the results were used to evaluate the quality of the data from the groundwater samples. Field blank samples rarely contained detectable concentrations of any constituent, indicating that contamination from sample collection or analysis was not a significant source of bias in the data for the groundwater samples. More than 99 percent of the replicate pair samples were within acceptable limits of variability. Matrix-spike sample recoveries were within the acceptable range (70 to 130 percent) for approximately 91 percent of the compounds.

\n\n
\n\n

This study did not evaluate the quality of water delivered to consumers. After withdrawal, groundwater typically is treated, disinfected, and (or) blended with other waters to maintain water quality. Regulatory benchmarks apply to water that is delivered to the consumer, not to untreated groundwater. However, to provide some context for the results, concentrations of constituents measured in the untreated groundwater were compared with regulatory and non-regulatory health-based benchmarks established by the U.S. Environmental Protection Agency (USEPA) and CDPH, and to non-health-based benchmarks established for aesthetic concerns by the CDPH. Comparisons between data collected for this study and benchmarks for drinking water are for illustrative purposes only and are not indicative of compliance or non-compliance with those benchmarks.

\n\n
\n\n

All concentrations of organic constituents from grid sites sampled in the KLAM study unit were less than health-based benchmarks. In total, VOCs were detected in 16 of the 38 grid sites sampled (approximately 42 percent), pesticides and pesticide degradates were detected in 8 grid sites (about 21 percent), and microbial indicators were detected in 14 grid sites (approximately 37 percent).

\n\n
\n\n

Inorganic constituents (trace elements, major and minor ions, nutrients, and uranium and other radioactive constituents) and microbial indicators were sampled for at 38 grid sites, and all concentrations were less than health-based benchmarks, with the exception of one detection of boron greater than the CDPH notification level of 1,000 micrograms per liter (μg/L). Generally, concentrations of inorganic constituents with non-health-based benchmarks (iron, manganese, chloride, and TDS) were less than the CDPH secondary maximum contaminant level (SMCL-CA). Exceptions include three detections of iron greater than the SMCL-CA of 300 μg/L, four detections of manganese greater than the SMCL-CA of 50 μg/L, one detection of chloride greater than the recommended SMCL-CA of 250 μg/L, and one detection of TDS greater than the recommended SMCL-CA of 500 μg/L.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds803","collaboration":"A product of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program; Prepared in cooperation with the California State Water Resources Control Board","usgsCitation":"Mathany, T., and Belitz, K., 2014, Groundwater-quality data in the Klamath Mountains study unit, 2010: results from the California GAMA Program: U.S. Geological Survey Data Series 803, x, 82 p., https://doi.org/10.3133/ds803.","productDescription":"x, 82 p.","numberOfPages":"96","ipdsId":"IP-036089","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":285118,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/803/pdf/ds803.pdf"},{"id":285117,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/ds/803/"},{"id":285119,"rank":3,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/ds803.jpg"},{"id":504828,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_99759.htm","linkFileType":{"id":5,"text":"html"}}],"projection":"Albers Equal Area Conic Projection","country":"United States","state":"California","otherGeospatial":"Klamath Mountains study unit","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.1,\n 42\n ],\n [\n -121.8,\n 42\n ],\n [\n -121.8,\n 40.1167\n ],\n [\n -124.1,\n 40.1167\n ],\n [\n -124.1,\n 42\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53517044e4b05569d805a245","contributors":{"authors":[{"text":"Mathany, Timothy M. 0000-0002-4747-5113","orcid":"https://orcid.org/0000-0002-4747-5113","contributorId":99949,"corporation":false,"usgs":true,"family":"Mathany","given":"Timothy M.","affiliations":[],"preferred":false,"id":485868,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Belitz, Kenneth 0000-0003-4481-2345 kbelitz@usgs.gov","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":442,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","email":"kbelitz@usgs.gov","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true}],"preferred":true,"id":485867,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70161768,"text":"70161768 - 2014 - Strength of evidence for the effects of feral cats on insular wildlife: The Club Med Syndrome Part II","interactions":[],"lastModifiedDate":"2018-03-23T14:23:52","indexId":"70161768","displayToPublicDate":"2014-03-20T14:30:00","publicationYear":"2014","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Strength of evidence for the effects of feral cats on insular wildlife: The Club Med Syndrome Part II","docAbstract":"

Various types of evidence have been promulgated as proof for the effects of feral cats on wildlife, typically including numerous studies on predation inferred from diet, mortality attributed to pathogens, and photographic or videographic documentation. The strength of these types of evidence is often short of conclusive. For example, studies of predation inferred from diet provide weak evidence for two reasons: 1) they cannot differentiate depredation from scavenging by feral cats, and 2) they cannot address population-level effects on wildlife because it is rarely understood if mortality acts in compensatory or additive manner. Likewise, pathogens may cause mortality of individuals, but population-level effects of pathogens are rarely known. Photographic or videographic documentation provides direct ‘smoking gun’ evidence that may be useful for positive identification of depredation by cats, or identification of prey designated as threatened or endangered species. However, the most direct and compelling evidence comes from examples where feral cats have been entirely removed from islands. In many cases, several species of seabirds as well as other wildlife have recovered after the complete removal of cats. Where possible, the experimental removal of cats would provide the most conclusive proof of effects on wildlife populations. In other cases where cat removal is not feasible, modeling based on predation rates and life history parameters of species may be the only means of assessing population-level effects on wildlife. Understanding population-level effects of feral cats on wildlife will ultimately be necessary to resolve long-standing wildlife management issues.

","conferenceTitle":"26th Vertebrate Pest Conference","conferenceDate":"March 3, 2014","conferenceLocation":"Waikoloa, HI","language":"English","publisher":"University of California, Davis","usgsCitation":"Hess, S.C., 2014, Strength of evidence for the effects of feral cats on insular wildlife: The Club Med Syndrome Part II, 26th Vertebrate Pest Conference, Waikoloa, HI, March 3, 2014, p. 211-216.","productDescription":"5 p.","startPage":"211","endPage":"216","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-057984","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":326239,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":326238,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.vpconference.org/Proceedings_of_the_Vertebrate_Pest_Conference/"}],"country":"United States","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"57a9ad73e4b05e859bdfbb17","contributors":{"authors":[{"text":"Hess, Steve C. 0000-0001-6403-9922 shess@usgs.gov","orcid":"https://orcid.org/0000-0001-6403-9922","contributorId":150366,"corporation":false,"usgs":true,"family":"Hess","given":"Steve","email":"shess@usgs.gov","middleInitial":"C.","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":587717,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70048964,"text":"sir20105070J - 2014 - A deposit model for carbonatite and peralkaline intrusion-related rare earth element deposits","interactions":[],"lastModifiedDate":"2022-12-09T23:54:22.187043","indexId":"sir20105070J","displayToPublicDate":"2014-03-03T14:19:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2010-5070","chapter":"J","title":"A deposit model for carbonatite and peralkaline intrusion-related rare earth element deposits","docAbstract":"

Carbonatite and alkaline intrusive complexes, as well as their weathering products, are the primary sources of rare earth elements. A wide variety of other commodities have been exploited from carbonatites and alkaline igneous rocks including niobium, phosphate, titanium, vermiculite, barite, fluorite, copper, calcite, and zirconium. Other elements enriched in these deposits include manganese, strontium, tantalum, thorium, vanadium, and uranium. Carbonatite and peralkaline intrusion-related rare earth element deposits are presented together in this report because of the spatial, and potentially genetic, association between carbonatite and alkaline rocks. Although these rock types occur together at many locations, carbonatite and peralkaline intrusion-related rare earth element deposits are not generally found together.

\n

Carbonatite hosted rare earth element deposits are found throughout the world, but currently only five are being mined for rare earth elements: Bayan Obo, Daluxiang, Maoniuping, and Weishan deposits in China and the Mountain Pass deposit in California, United States. These deposits are enriched in light rare earth elements, including lanthanum, cerium, praseodynium, and neodynium. The principal rare earth element-minerals associated with carbonatites are fluocarbonates (bastnäsite, parisite, and synchysite), hydrated carbonates (ancylite), and phosphates (monazite) with bastnäsite being the primary ore mineral. Calcite and dolomite are the primary gangue minerals. At present, the only rare earth element production from a peralkaline intrusion-related deposit is as a byproduct commodity at the Lovozero deposit in Russia. Important rare earth element minerals found in various deposits include apatite, eudialyte, loparite, gittinsite, xenotime, gadolinite, monazite, bastnäsite, kainosite, mosandrite, britholite, allanite, fergusonite, and zircon, and these minerals tend to be enriched in heavy rare earth elements.

\n

Carbonatite and alkaline intrusive complexes are derived from partial melts of mantle material, and neodymium isotopic data are consistent with the rare earth elements being derived from the parental magma. Deposits and these associated rock types tend to occur within stable continental tectonic units, in areas defined as shields, cratons, and crystalline blocks; they are generally associated with intracontinental rift and fault systems. Protracted fractional crystallization of the magma leads to enrichment in rare earth elements and other incompatible elements. Rare earth element mineralization associated with carbonatites can occur as either primary mineral phases or as mineralization associated with late stage orthomagmatic fluids. Rare earth element mineralization associated with alkaline intrusive complexes may occur as primary phases in magmatic layered complexes or as late-stage dikes and veins.

\n

The greatest environmental challenges associated with carbonatite and peralkaline intrusion-related rare earth element deposits center on the associated uranium and thorium. Considerable uncertainty exists around the toxicity of rare earth elements and warrants further investigation. The acid-generating potential of carbonatites and peralkaline intrusion-related deposits is low due to the dominance of carbonate minerals in carbonatite deposits, the presence of feldspars and minor calcite within the alkaline intrusion deposits, and only minor quantities of potentially acid-generating sulfides. Therefore, acid-drainage issues are not likely to be a major concern associated with these deposits. Uranium has the potential to be recovered as a byproduct, which would mitigate some of its environmental effects. However, thorium will likely remain a waste-stream product that will require management since progress is not being made towards the development of thorium-based nuclear reactors in the United States or other large scale commercial uses. Because some deposits are rich in fluorine and beryllium, these elements may be of environmental concern in certain locations.

","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Mineral deposit models for resource assessment (Scientific Investigations Report 2010-5070)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20105070J","usgsCitation":"Verplanck, P.L., Van Gosen, B.S., Seal, R., and McCafferty, A.E., 2014, A deposit model for carbonatite and peralkaline intrusion-related rare earth element deposits: U.S. Geological Survey Scientific Investigations Report 2010-5070, x, 58 p., https://doi.org/10.3133/sir20105070J.","productDescription":"x, 58 p.","numberOfPages":"72","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-039549","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":283180,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2010/5070/j/pdf/sir2010-5070J.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"Report"},{"id":283179,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/sir/2010/5070/j/"},{"id":283181,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/sir20105070j.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd49bae4b0b290850ef5c3","contributors":{"authors":[{"text":"Verplanck, Philip L. 0000-0002-3653-6419 plv@usgs.gov","orcid":"https://orcid.org/0000-0002-3653-6419","contributorId":728,"corporation":false,"usgs":true,"family":"Verplanck","given":"Philip","email":"plv@usgs.gov","middleInitial":"L.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":485887,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Gosen, Bradley S. 0000-0003-4214-3811 bvangose@usgs.gov","orcid":"https://orcid.org/0000-0003-4214-3811","contributorId":1174,"corporation":false,"usgs":true,"family":"Van Gosen","given":"Bradley","email":"bvangose@usgs.gov","middleInitial":"S.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":true,"id":485889,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Seal, Robert R. II 0000-0003-0901-2529 rseal@usgs.gov","orcid":"https://orcid.org/0000-0003-0901-2529","contributorId":397,"corporation":false,"usgs":true,"family":"Seal","given":"Robert R.","suffix":"II","email":"rseal@usgs.gov","affiliations":[],"preferred":false,"id":485886,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCafferty, Anne E. 0000-0001-5574-9201 anne@usgs.gov","orcid":"https://orcid.org/0000-0001-5574-9201","contributorId":1120,"corporation":false,"usgs":true,"family":"McCafferty","given":"Anne","email":"anne@usgs.gov","middleInitial":"E.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":485888,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70099275,"text":"70099275 - 2014 - Aspidoscelis deppii (black-bellied racerunner). Predation by turkey vulture.","interactions":[],"lastModifiedDate":"2017-05-03T10:36:29","indexId":"70099275","displayToPublicDate":"2014-03-01T10:13:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1898,"text":"Herpetological Review","active":true,"publicationSubtype":{"id":10}},"title":"Aspidoscelis deppii (black-bellied racerunner). Predation by turkey vulture.","docAbstract":"

Aspidoscelis deppii is widely distributed from Veracruz and Michoacan, Mexico, to Costa Rica (Köhler et al. 2006. The Amphibians and Reptiles of El Salvador, Krieger Publishing Company, Malabar, Florida. 238 pp.). Neotropical lizards are abundant and common prey to all classes of terrestrial vertebrates, and bird predation of lizards is well known. The Turkey Vulture (Carthartes aura) is widely distributed from southern Canada south to South America and is present throughout the entire range of A. deppii, where it occupies a variety of open and forested habitats and feeds opportunistically on a wide range of wild and domestic carrion. While almost exclusively a scavenger, this species is known to rarely kill small animals or invertebrates (Kirk and Mossman 1998. In A. Poole [ed.], The Birds of North America Online. Cornell Lab of Ornithology, Ithaca; accessed 15 August 2013). An adult Turkey Vulture was collected during avian control to minimize wildlife hazards at the Aeropuerto Internacional de El Salvador (ca. 50 km SE of San Salvador, 13.4408°N 89.0556°W; datum WGS84) on 10 July 2012 and subsequently cataloged (USNM 646876) in the Bird Division at the National Museum of Natural History (NMNH) in Washington, DC. Dissection during preparation of the bird as a museum specimen revealed a male A. deppii (ca. 56 mm SVL) in the stomach. It was cataloged at the NMNH in the Division of Amphibians and Reptiles (USNM 580989). Tissue samples were removed from both the lizard and the bird and deposited in the biorepository at the NMNH. To the best of our knowledge, this is the first documented record identifying A. deppii as a prey item of the Turkey Vulture.

","language":"English","publisher":"Society for the Study of Amphibians and Reptiles","usgsCitation":"Reynolds, R.P., and Gebhard, C.A., 2014, Aspidoscelis deppii (black-bellied racerunner). Predation by turkey vulture.: Herpetological Review, v. 45, no. 1, p. 124-124.","productDescription":"1 p.","startPage":"124","endPage":"124","ipdsId":"IP-052242","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":284908,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":331809,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://ssarherps.org/herpetological-review-pdfs/"}],"country":"El Salvador","otherGeospatial":"Aeropuerto Internacional de El Salvador","volume":"45","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53517022e4b05569d805a15c","contributors":{"authors":[{"text":"Reynolds, Robert P. rpreynolds@usgs.gov","contributorId":3561,"corporation":false,"usgs":true,"family":"Reynolds","given":"Robert","email":"rpreynolds@usgs.gov","middleInitial":"P.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":491932,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gebhard, Christina A.","contributorId":54107,"corporation":false,"usgs":true,"family":"Gebhard","given":"Christina","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":491933,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70102893,"text":"70102893 - 2014 - Statistical evaluation of variables affecting occurrence of hydrocarbons in aquifers used for public supply, California","interactions":[],"lastModifiedDate":"2018-06-08T14:21:34","indexId":"70102893","displayToPublicDate":"2014-03-01T09:23:10","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2529,"text":"Journal of the American Water Resources Association","active":true,"publicationSubtype":{"id":10}},"title":"Statistical evaluation of variables affecting occurrence of hydrocarbons in aquifers used for public supply, California","docAbstract":"The variables affecting the occurrence of hydrocarbons in aquifers used for public supply in California were assessed based on statistical evaluation of three large statewide datasets; gasoline oxygenates also were analyzed for comparison with hydrocarbons. Benzene is the most frequently detected (1.7%) compound among 17 hydrocarbons analyzed at generally low concentrations (median detected concentration 0.024 μg/l) in groundwater used for public supply in California; methyl tert-butyl ether (MTBE) is the most frequently detected (5.8%) compound among seven oxygenates analyzed (median detected concentration 0.1 μg/l). At aquifer depths used for public supply, hydrocarbons and MTBE rarely co-occur and are generally related to different variables; in shallower groundwater, co-occurrence is more frequent and there are similar relations to the density or proximity of potential sources. Benzene concentrations are most strongly correlated with reducing conditions, regardless of groundwater age and depth. Multiple lines of evidence indicate that benzene and other hydrocarbons detected in old, deep, and/or brackish groundwater result from geogenic sources of oil and gas. However, in recently recharged (since ~1950), generally shallower groundwater, higher concentrations and detection frequencies of benzene and hydrocarbons were associated with a greater proportion of commercial land use surrounding the well, likely reflecting effects of anthropogenic sources, particularly in combination with reducing conditions.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of the American Water Resources Association","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1111/jawr.12129","usgsCitation":"Landon, M.K., Burton, C., Davis, T., Belitz, K., and Johnson, T., 2014, Statistical evaluation of variables affecting occurrence of hydrocarbons in aquifers used for public supply, California: Journal of the American Water Resources Association, v. 50, no. 1, p. 179-195, https://doi.org/10.1111/jawr.12129.","productDescription":"17 p.","startPage":"179","endPage":"195","ipdsId":"IP-028405","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":286680,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":286619,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1111/jawr.12129"}],"country":"United States","state":"California","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -124.41,32.53 ], [ -124.41,42.0 ], [ -114.13,42.0 ], [ -114.13,32.53 ], [ -124.41,32.53 ] ] ] } } ] }","volume":"50","issue":"1","noUsgsAuthors":false,"publicationDate":"2013-10-08","publicationStatus":"PW","scienceBaseUri":"535f7874e4b078dca33ae384","contributors":{"authors":[{"text":"Landon, Matthew K. 0000-0002-5766-0494 landon@usgs.gov","orcid":"https://orcid.org/0000-0002-5766-0494","contributorId":392,"corporation":false,"usgs":true,"family":"Landon","given":"Matthew","email":"landon@usgs.gov","middleInitial":"K.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":493077,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burton, Carmen A. 0000-0002-6381-8833","orcid":"https://orcid.org/0000-0002-6381-8833","contributorId":41793,"corporation":false,"usgs":true,"family":"Burton","given":"Carmen A.","affiliations":[],"preferred":false,"id":493079,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Davis, Tracy A. 0000-0003-0253-6661","orcid":"https://orcid.org/0000-0003-0253-6661","contributorId":59339,"corporation":false,"usgs":true,"family":"Davis","given":"Tracy A.","affiliations":[],"preferred":false,"id":493080,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Belitz, Kenneth 0000-0003-4481-2345 kbelitz@usgs.gov","orcid":"https://orcid.org/0000-0003-4481-2345","contributorId":442,"corporation":false,"usgs":true,"family":"Belitz","given":"Kenneth","email":"kbelitz@usgs.gov","affiliations":[{"id":503,"text":"Office of Water Quality","active":true,"usgs":true},{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":376,"text":"Massachusetts Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":493078,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Johnson, Tyler D. 0000-0002-7334-9188","orcid":"https://orcid.org/0000-0002-7334-9188","contributorId":64366,"corporation":false,"usgs":true,"family":"Johnson","given":"Tyler D.","affiliations":[],"preferred":false,"id":493081,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70074264,"text":"70074264 - 2014 - Seismological analyses of the 2010 March 11, Pichilemu, Chile Mw 7.0 and Mw 6.9 coastal intraplate earthquakes","interactions":[],"lastModifiedDate":"2014-03-04T16:23:23","indexId":"70074264","displayToPublicDate":"2014-03-01T09:10:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1803,"text":"Geophysical Journal International","active":true,"publicationSubtype":{"id":10}},"title":"Seismological analyses of the 2010 March 11, Pichilemu, Chile Mw 7.0 and Mw 6.9 coastal intraplate earthquakes","docAbstract":"On 2010 March 11, a sequence of large, shallow continental crust earthquakes shook central Chile. Two normal faulting events with magnitudes around Mw 7.0 and Mw 6.9 occurred just 15 min apart, located near the town of Pichilemu. These kinds of large intraplate, inland crustal earthquakes are rare above the Chilean subduction zone, and it is important to better understand their relationship with the 2010 February 27, Mw 8.8, Maule earthquake, which ruptured the adjacent megathrust plate boundary. We present a broad seismological analysis of these earthquakes by using both teleseismic and regional data. We compute seismic moment tensors for both events via a W-phase inversion, and test sensitivities to various inversion parameters in order to assess the stability of the solutions. The first event, at 14 hr 39 min GMT, is well constrained, displaying a fault plane with strike of N145°E, and a preferred dip angle of 55°SW, consistent with the trend of aftershock locations and other published results. Teleseismic finite-fault inversions for this event show a large slip zone along the southern part of the fault, correlating well with the reported spatial density of aftershocks. The second earthquake (14 hr 55 min GMT) appears to have ruptured a fault branching southward from the previous ruptured fault, within the hanging wall of the first event. Modelling seismograms at regional to teleseismic distances (Δ > 10°) is quite challenging because the observed seismic wave fields of both events overlap, increasing apparent complexity for the second earthquake. We perform both point- and extended-source inversions at regional and teleseismic distances, assessing model sensitivities resulting from variations in fault orientation, dimension, and hypocentre location. Results show that the focal mechanism for the second event features a steeper dip angle and a strike rotated slightly clockwise with respect to the previous event. This kind of geological fault configuration, with secondary rupture in the hanging wall of a large normal fault, is commonly observed in extensional geological regimes. We propose that both earthquakes form part of a typical normal fault diverging splay, where the secondary fault connects to the main fault at depth. To ascertain more information on the spatial and temporal details of slip for both events, we gathered near-fault seismological and geodetic data. Through forward modelling of near-fault synthetic seismograms we build a kinematic k−2 earthquake source model with spatially distributed slip on the fault that, to first-order, explains both coseismic static displacement GPS vectors and short-period seismometer observations at the closest sites. As expected, the results for the first event agree with the focal mechanism derived from teleseismic modelling, with a magnitude Mw 6.97. Similarly, near-fault modelling for the second event suggests rupture along a normal fault, Mw 6.90, characterized by a steeper dip angle (dip = 74°) and a strike clockwise rotated (strike = 155°) with respect to the previous event.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Geophysical Journal International","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Oxford University Press","doi":"10.1093/gji/ggt513","usgsCitation":"Ruiz, J.A., Hayes, G., Carrizo, D., Kanamori, H., Socquet, A., and Comte, D., 2014, Seismological analyses of the 2010 March 11, Pichilemu, Chile Mw 7.0 and Mw 6.9 coastal intraplate earthquakes: Geophysical Journal International, v. 196, no. 3, 21 p., https://doi.org/10.1093/gji/ggt513.","productDescription":"21 p.","ipdsId":"IP-053432","costCenters":[{"id":415,"text":"National Earthquake Information Center","active":false,"usgs":true}],"links":[{"id":473147,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/gji/ggt513","text":"Publisher Index Page"},{"id":283363,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":281636,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1093/gji/ggt513"}],"country":"Chile","city":"Pichilemu","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -72.056,-34.586 ], [ -72.056,-34.165 ], [ -71.746,-34.165 ], [ -71.746,-34.586 ], [ -72.056,-34.586 ] ] ] } } ] }","volume":"196","issue":"3","noUsgsAuthors":false,"publicationDate":"2014-02-07","publicationStatus":"PW","scienceBaseUri":"5351705fe4b05569d805a39a","contributors":{"authors":[{"text":"Ruiz, Javier A.","contributorId":39287,"corporation":false,"usgs":true,"family":"Ruiz","given":"Javier","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":489457,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hayes, Gavin P. 0000-0003-3323-0112","orcid":"https://orcid.org/0000-0003-3323-0112","contributorId":6157,"corporation":false,"usgs":true,"family":"Hayes","given":"Gavin P.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":489455,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carrizo, Daniel","contributorId":36456,"corporation":false,"usgs":true,"family":"Carrizo","given":"Daniel","email":"","affiliations":[],"preferred":false,"id":489456,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kanamori, Hiroo","contributorId":106120,"corporation":false,"usgs":true,"family":"Kanamori","given":"Hiroo","affiliations":[],"preferred":false,"id":489460,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Socquet, Anne","contributorId":65764,"corporation":false,"usgs":true,"family":"Socquet","given":"Anne","email":"","affiliations":[],"preferred":false,"id":489459,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Comte, Diana","contributorId":40514,"corporation":false,"usgs":true,"family":"Comte","given":"Diana","email":"","affiliations":[],"preferred":false,"id":489458,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70188364,"text":"70188364 - 2014 - The profound reach of the 11 April 2012 M 8.6 Indian Ocean earthquake: Short‐term global triggering followed by a longer‐term global shadow","interactions":[],"lastModifiedDate":"2017-06-07T11:46:41","indexId":"70188364","displayToPublicDate":"2014-03-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"The profound reach of the 11 April 2012 M 8.6 Indian Ocean earthquake: Short‐term global triggering followed by a longer‐term global shadow","docAbstract":"

The 11 April 2012 M 8.6 Indian Ocean earthquake was an unusually large intraoceanic strike‐slip event. For several days, the global M≥4.5 and M≥6.5 seismicity rate at remote distances (i.e., thousands of kilometers from the mainshock) was elevated. The strike‐slip mainshock appears through its Love waves to have triggered a global burst of strike‐slip aftershocks over several days. But the M≥6.5 rate subsequently dropped to zero for the succeeding 95 days, although the M≤6.0 global rate was close to background during this period. Such an extended period without an M≥6.5 event has happened rarely over the past century, and never after a large mainshock. Quiescent periods following previous large (M≥8) mainshocks over the past century are either much shorter or begin so long after a given mainshock that no physical interpretation is warranted. The 2012 mainshock is unique in terms of both the short‐lived global increase and subsequent long quiescent period. We believe that the two components are linked and interpret this pattern as the product of dynamic stressing of a global system of faults. Transient dynamic stresses can encourage short‐term triggering, but, paradoxically, it can also inhibit rupture temporarily until background tectonic loading restores the system to its premainshock stress levels.

","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120130078","usgsCitation":"Pollitz, F., Burgmann, R., Stein, R.S., and Sevilgen, V., 2014, The profound reach of the 11 April 2012 M 8.6 Indian Ocean earthquake: Short‐term global triggering followed by a longer‐term global shadow: Bulletin of the Seismological Society of America, v. 104, no. 2, p. 972-984, https://doi.org/10.1785/0120130078.","productDescription":"13 p.","startPage":"972","endPage":"984","ipdsId":"IP-044296","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":342227,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"104","issue":"2","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2014-03-11","publicationStatus":"PW","scienceBaseUri":"593910b4e4b0764e6c5e88dc","contributors":{"authors":[{"text":"Pollitz, Frederick 0000-0002-4060-2706 fpollitz@usgs.gov","orcid":"https://orcid.org/0000-0002-4060-2706","contributorId":139578,"corporation":false,"usgs":true,"family":"Pollitz","given":"Frederick","email":"fpollitz@usgs.gov","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":697406,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Burgmann, Roland","contributorId":192700,"corporation":false,"usgs":false,"family":"Burgmann","given":"Roland","affiliations":[],"preferred":false,"id":697409,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stein, Ross S. 0000-0001-7586-3933 rstein@usgs.gov","orcid":"https://orcid.org/0000-0001-7586-3933","contributorId":2604,"corporation":false,"usgs":true,"family":"Stein","given":"Ross","email":"rstein@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":697407,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Sevilgen, Volkan vsevilgen@usgs.gov","contributorId":3254,"corporation":false,"usgs":true,"family":"Sevilgen","given":"Volkan","email":"vsevilgen@usgs.gov","affiliations":[],"preferred":true,"id":697408,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70154815,"text":"70154815 - 2014 - Mercury bioaccumulation in Southern Appalachian birds, assessed through feather concentrations","interactions":[],"lastModifiedDate":"2015-08-13T13:55:54","indexId":"70154815","displayToPublicDate":"2014-03-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1479,"text":"Ecotoxicology","active":true,"publicationSubtype":{"id":10}},"title":"Mercury bioaccumulation in Southern Appalachian birds, assessed through feather concentrations","docAbstract":"

Mercury contamination in wildlife has rarely been studied in the Southern Appalachians despite high deposition rates in the region. From 2006 to 2008 we sampled feathers from 458 birds representing 32 species in the Southern Appalachians for total mercury and stable isotope δ 15N. Mercury concentrations (mean ± SE) averaged 0.46 ± 0.02 μg g−1 (range 0.01–3.74 μg g−1). Twelve of 32 species had individuals (7 % of all birds sampled) with mercury concentrations higher than 1 μg g−1. Mercury concentrations were 17 % higher in juveniles compared to adults (n = 454). In adults, invertivores has higher mercury levels compared to omnivores. Mercury was highest at low-elevation sites near water, however mercury was detected in all birds, including those in the high elevations (1,000–2,000 m). Relative trophic position, calculated from δ 15N, ranged from 2.13 to 4.87 across all birds. We fitted linear mixed-effects models to the data separately for juveniles and year-round resident adults. In adults, mercury concentrations were 2.4 times higher in invertivores compared to omnivores. Trophic position was the main effect explaining mercury levels in juveniles, with an estimated 0.18 ± 0.08 μg g−1 increase in feather mercury for each one unit rise in trophic position. Our research demonstrates that mercury is biomagnifying in birds within this terrestrial mountainous system, and further research is warranted for animals foraging at higher trophic levels, particularly those associated with aquatic environments downslope from montane areas receiving high mercury deposition.

","language":"English","doi":"10.1007/s10646-013-1174-6","usgsCitation":"Keller, R.H., Xie, L., Buchwalter, D.B., Franzreb, K.E., and Simons, T.R., 2014, Mercury bioaccumulation in Southern Appalachian birds, assessed through feather concentrations: Ecotoxicology, v. 23, no. 2, p. 304-316, https://doi.org/10.1007/s10646-013-1174-6.","productDescription":"13 p.","startPage":"304","endPage":"316","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-044870","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":306667,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","issue":"2","publishingServiceCenter":{"id":8,"text":"Raleigh PSC"},"noUsgsAuthors":false,"publicationDate":"2014-01-14","publicationStatus":"PW","scienceBaseUri":"55cdbfb8e4b08400b1fe1414","contributors":{"authors":[{"text":"Keller, Rebecca Hylton","contributorId":12213,"corporation":false,"usgs":true,"family":"Keller","given":"Rebecca","email":"","middleInitial":"Hylton","affiliations":[],"preferred":false,"id":568025,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xie, Lingtian","contributorId":65209,"corporation":false,"usgs":true,"family":"Xie","given":"Lingtian","email":"","affiliations":[],"preferred":false,"id":568026,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Buchwalter, David B.","contributorId":11927,"corporation":false,"usgs":true,"family":"Buchwalter","given":"David","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":568027,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Franzreb, Kathleen E.","contributorId":146487,"corporation":false,"usgs":false,"family":"Franzreb","given":"Kathleen","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":568028,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Simons, Theodore R. 0000-0002-1884-6229 tsimons@usgs.gov","orcid":"https://orcid.org/0000-0002-1884-6229","contributorId":2623,"corporation":false,"usgs":true,"family":"Simons","given":"Theodore","email":"tsimons@usgs.gov","middleInitial":"R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":564229,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70160811,"text":"70160811 - 2014 - Benthic prey fish assessment, Lake Ontario 2013","interactions":[],"lastModifiedDate":"2020-03-05T12:20:58","indexId":"70160811","displayToPublicDate":"2014-03-01T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":5114,"text":"NYSDEC Lake Ontario Annual Report ","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"2013","chapter":"12","title":"Benthic prey fish assessment, Lake Ontario 2013","docAbstract":"

The 2013 benthic fish assessment was delayed and shortened as a result of the U.S. Government shutdown, however the assessment collected 51 of the 62 planned bottom trawls.

Over the past 34 years, Slimy Sculpin abundance in Lake Ontario has fluctuated, but ultimately decreased by two orders of magnitude, with a substantial decline occurring in the past 10 years. The 2013 Slimy Sculpin mean bottom trawl catch density (0.001 ind.·m-2, s.d.= 0.0017, n = 52) and mean biomass density (0.015 g·m-2 , s.d.= 0.038, n = 52) were the lowest recorded in the 27 years of sampling using the original bottom trawl design. From 2011-2013, the Slimy Sculpin density and biomass density has decreased by approximately 50% each year. Spring bottom trawl catches illustrate Slimy Sculpin and Round Goby Neogobius melanostoma winter habitat overlaps for as much as 7 months out of a year, providing opportunities for competition and predation. Invasive species, salmonid piscivory, and declines in native benthic invertebrates are likely all important drivers of Slimy Sculpin population dynamics in Lake Ontario.

Deepwater Sculpin Myoxocephalus thompsonii, considered rare or absent from Lake Ontario for 30 years, have generally increased over the past eight years. For the first time since they were caught in this assessment, Deepwater Sculpin density and biomass density estimates declined from the previous year. The 2013 abundance and density estimates for trawls covering the standard depths from 60m to 150m was 0.0001 fish per square meter and 0.0028 grams per square meter. In 2013, very few small (< 80 mm) Deepwater Sculpin were caught and most sculpin were at sites of 150 meters or greater, which is in contrast to previous years when juvenile fish were caught around 80-100 meters. The reduced effort and late seasonal timing of the 2013 assessment make it difficult to have high confidence in declines observed in 2013, however observed Alewife Alosa psuedoharengus abundance increases and reduced juvenile Deepwater Sculpin catches are consistent with the hypothesis that Alewife negatively influence Deepwater Sculpin recruitment.

Nonnative Round Gobies were first detected in the USGS/NYSDEC Lake Ontario spring Alewife assessment in 2002. Since that assessment, observations indicate their population has expanded and they are now found along the entire south shore of Lake Ontario, with the highest densities in U.S. waters just east of the Niagara River confluence. In the 2013 spring-based assessment, both the abundance and weight indices increased slightly as compared to 2012. The number index value of 16.6 was 30% of the maximum number observed in 2008 when the number index was 95.2. Round Goby density estimates from the 2013 fall benthic prey fish survey were 33 times greater than fall Slimy Sculpin density, indicating Round Goby are now the dominant Lake Ontario benthic prey fish.

","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"2013 Annual report: Bureau of Fisheries, Lake Ontario unit and St. Lawrence River unit, to the Great Lakes Fishery Commission’s Lake Ontario Committee","largerWorkSubtype":{"id":2,"text":"State or Local Government Series"},"conferenceTitle":"Lake Ontario Committee Meeting","conferenceDate":"March 26-27, 2014","conferenceLocation":"Windsor, ON","language":"English","publisher":"New York State Department of Environmental Conservation","publisherLocation":"Albany, NY","usgsCitation":"Weidel, B., Walsh, M., and Connerton, M., 2014, Benthic prey fish assessment, Lake Ontario 2013: NYSDEC Lake Ontario Annual Report 2013, 9 p.","productDescription":"9 p.","startPage":"12-16","endPage":"12-24","ipdsId":"IP-055337","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":342382,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":313103,"rank":2,"type":{"id":15,"text":"Index Page"},"url":"https://purl.nysed.gov/nysl/889897048"}],"country":"Canada, United States","otherGeospatial":"Lake Ontario","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -79.881591796875,\n 43.281204464332745\n ],\n [\n -79.573974609375,\n 43.22519255488632\n ],\n [\n -79.3817138671875,\n 43.197167282501276\n ],\n [\n -79.2718505859375,\n 43.18515250937298\n ],\n [\n -79.1619873046875,\n 43.26920624914964\n ],\n [\n -79.0411376953125,\n 43.281204464332745\n ],\n [\n -78.662109375,\n 43.37311218382002\n ],\n [\n -78.2171630859375,\n 43.41302868475145\n ],\n [\n -77.991943359375,\n 43.393073720674415\n ],\n [\n -77.8765869140625,\n 43.38109758727857\n ],\n [\n -77.7117919921875,\n 43.329173667843904\n ],\n [\n -77.607421875,\n 43.27720532212024\n ],\n [\n -77.5360107421875,\n 43.25320494908846\n ],\n [\n -77.4591064453125,\n 43.27320591705845\n ],\n [\n -77.2998046875,\n 43.297198404646366\n ],\n [\n -77.14050292968749,\n 43.29320031385282\n ],\n [\n -77.0745849609375,\n 43.29320031385282\n ],\n [\n -76.97021484375,\n 43.27320591705845\n ],\n [\n -76.849365234375,\n 43.313188139196406\n ],\n [\n -76.7010498046875,\n 43.36512572875844\n ],\n [\n -76.6021728515625,\n 43.440954591707445\n ],\n [\n -76.497802734375,\n 43.51668853502906\n ],\n [\n -76.37695312499999,\n 43.53660274231031\n ],\n [\n -76.2725830078125,\n 43.52465500687185\n ],\n [\n -76.256103515625,\n 43.55252937447483\n ],\n [\n -76.22314453125,\n 43.600284023536325\n ],\n [\n -76.190185546875,\n 43.67581809328341\n ],\n [\n -76.2506103515625,\n 43.75919263886012\n ],\n [\n -76.201171875,\n 43.929549935614595\n ],\n [\n -76.2066650390625,\n 43.96119063892024\n ],\n [\n -76.431884765625,\n 44.07574700247845\n ],\n [\n -76.5362548828125,\n 44.13885576756881\n ],\n [\n -76.6021728515625,\n 44.13885576756881\n ],\n [\n -76.66259765625,\n 44.10336537791152\n ],\n [\n -76.7559814453125,\n 44.071800467511565\n ],\n [\n -76.83837890625,\n 44.05995928349327\n ],\n [\n -76.8878173828125,\n 43.99676629896825\n ],\n [\n -76.9207763671875,\n 43.98095752608484\n ],\n [\n -76.871337890625,\n 43.97305156068593\n ],\n [\n -76.8109130859375,\n 43.957236472025635\n ],\n [\n -76.9976806640625,\n 43.854335770789575\n ],\n [\n -77.156982421875,\n 43.83848910616803\n ],\n [\n -77.266845703125,\n 43.862257524417934\n ],\n [\n -77.32177734375,\n 43.92163712834673\n ],\n [\n -77.420654296875,\n 43.9058083561574\n ],\n [\n -77.4920654296875,\n 43.909765943908\n ],\n [\n -77.6019287109375,\n 43.97700467496408\n ],\n [\n -77.7557373046875,\n 43.97700467496408\n ],\n [\n -77.89306640625,\n 43.957236472025635\n ],\n [\n -78.06884765624999,\n 43.94141717295212\n ],\n [\n -78.2281494140625,\n 43.91768033000405\n ],\n [\n -78.4698486328125,\n 43.88997537383687\n ],\n [\n -78.6566162109375,\n 43.854335770789575\n ],\n [\n -78.826904296875,\n 43.83848910616803\n ],\n [\n -79.0521240234375,\n 43.79885402720353\n ],\n [\n -79.189453125,\n 43.71950494269107\n ],\n [\n -79.2718505859375,\n 43.59630591596548\n ],\n [\n -79.43115234375,\n 43.60426186809618\n ],\n [\n -79.486083984375,\n 43.57641143300888\n ],\n [\n -79.573974609375,\n 43.52863784833001\n ],\n [\n -79.5904541015625,\n 43.50075243569041\n ],\n [\n -79.70581054687499,\n 43.369119087738554\n ],\n [\n -79.881591796875,\n 43.281204464332745\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"593fa839e4b0764e6c6279a5","contributors":{"authors":[{"text":"Weidel, Brian 0000-0001-6095-2773 bweidel@usgs.gov","orcid":"https://orcid.org/0000-0001-6095-2773","contributorId":2485,"corporation":false,"usgs":true,"family":"Weidel","given":"Brian","email":"bweidel@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":583993,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Walsh, Maureen 0000-0001-7846-5025 mwalsh@usgs.gov","orcid":"https://orcid.org/0000-0001-7846-5025","contributorId":3659,"corporation":false,"usgs":true,"family":"Walsh","given":"Maureen","email":"mwalsh@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":583992,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Connerton, Michael J.","contributorId":25495,"corporation":false,"usgs":false,"family":"Connerton","given":"Michael J.","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":583994,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70094946,"text":"70094946 - 2014 - Competitive interactions and resource partitioning between northern spotted owls and barred owls in western Oregon","interactions":[],"lastModifiedDate":"2016-07-18T21:48:48","indexId":"70094946","displayToPublicDate":"2014-02-26T09:52:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3773,"text":"Wildlife Monographs","active":true,"publicationSubtype":{"id":10}},"title":"Competitive interactions and resource partitioning between northern spotted owls and barred owls in western Oregon","docAbstract":"

The federally threatened northern spotted owl (Strix occidentalis caurina) is the focus of intensive conservation efforts that have led to much forested land being reserved as habitat for the owl and associated wildlife species throughout the Pacific Northwest of the United States. Recently, however, a relatively new threat to spotted owls has emerged in the form of an invasive competitor: the congeneric barred owl (S. varia). As barred owls have rapidly expanded their populations into the entire range of the northern spotted owl, mounting evidence indicates that they are displacing, hybridizing with, and even killing spotted owls. The range expansion by barred owls into western North America has made an already complex conservation issue even more contentious, and a lack of information on the ecological relationships between the 2 species has hampered recovery efforts for northern spotted owls. We investigated spatial relationships, habitat use, diets, survival, and reproduction of sympatric spotted owls and barred owls in western Oregon, USA, during 2007–2009. Our overall objective was to determine the potential for and possible consequences of competition for space, habitat, and food between these previously allopatric owl species. Our study included 29 spotted owls and 28 barred owls that were radio-marked in 36 neighboring territories and monitored over a 24-month period. Based on repeated surveys of both species, the number of territories occupied by pairs of barred owls in the 745-km2 study area (82) greatly outnumbered those occupied by pairs of spotted owls (15). Estimates of mean size of home ranges and core-use areas of spotted owls (1,843 ha and 305 ha, respectively) were 2–4 times larger than those of barred owls (581 ha and 188 ha, respectively). Individual spotted and barred owls in adjacent territories often had overlapping home ranges, but interspecific space sharing was largely restricted to broader foraging areas in the home range with minimal spatial overlap among core-use areas. We used an information-theoretic approach to rank discrete-choice models representing alternative hypotheses about the influence of forest conditions, topography, and interspecific interactions on species-specific patterns of nighttime resource selection. Spotted owls spent a disproportionate amount of time foraging on steep slopes in ravines dominated by old (>120 yr) conifer trees. Barred owls used available forest types more evenly than spotted owls, and were most strongly associated with patches of large hardwood and conifer trees that occupied relatively flat areas along streams. Spotted and barred owls differed in the relative use of old conifer forest (greater for spotted owls) and slope conditions (steeper slopes for spotted owls), but we found no evidence that the 2 species differed in their use of young, mature, and riparian-hardwood forest types. Mean overlap in proportional use of different forest types between individual spotted owls and barred owls in adjacent territories was 81% (range = 30–99%). The best model of habitat use for spotted owls indicated that the relative probability of a location being used was substantially reduced if the location was within or in close proximity to a core-use area of a barred owl. We used pellet analysis and measures of food-niche overlap to determine the potential for dietary competition between spatially associated pairs of spotted owls and barred owls. We identified 1,223 prey items from 15 territories occupied by spotted owls and 4,299 prey items from 24 territories occupied by barred owls. Diets of both species were dominated by nocturnal mammals, but diets of barred owls included many terrestrial, aquatic, and diurnal prey species that were rare or absent in diets of spotted owls. Northern flying squirrels (Glaucomys sabrinus), woodrats (Neotoma fuscipes, N. cinerea), and lagomorphs (Lepus americanus, Sylvilagus bachmani) were primary prey for both owl species, accounting for 81% and 49% of total dietary biomass for spotted owls and barred owls, respectively. Mean dietary overlap between pairs of spotted and barred owls in adjacent territories was moderate (42%; range = 28–70%). Barred owls displayed demographic superiority over spotted owls; annual survival probability of spotted owls from known-fate analyses (0.81, SE = 0.05) was lower than that of barred owls (0.92, SE = 0.04), and pairs of barred owls produced an average of 4.4 times more young than pairs of spotted owls over a 3-year period. We found a strong, positive relationship between seasonal (6-month) survival probabilities of both species and the proportion of old (>120 yr) conifer forest within individual home ranges, which suggested that availability of old forest was a potential limiting factor in the competitive relationship between these 2 species. The annual number of young produced by spotted owls increased linearly with increasing distance from a territory center of a pair of barred owls, and all spotted owls that attempted to nest within 1.5 km of a nest used by barred owls failed to successfully produce young. We identified strong associations between the presence of barred owls and the behavior and fitness potential of spotted owls, as shown by changes in movements, habitat use, and reproductive output of spotted owls exposed to different levels of spatial overlap with territorial barred owls. When viewed collectively, our results support the hypothesis that interference competition with barred owls for territorial space can constrain the availability of critical resources required for successful recruitment and reproduction of spotted owls. Availability of old forests and associated prey species appeared to be the most strongly limiting factors in the competitive relationship between these species, indicating that further loss of these conditions can lead to increases in competitive pressure. Our findings have broad implications for the conservation of spotted owls, as they suggest that spatial heterogeneity in vital rates may not arise solely because of differences among territories in the quality or abundance of forest habitat, but also because of the spatial distribution of a newly established competitor. Experimental removal of barred owls could be used to test this hypothesis and determine whether localized control of barred owl numbers is an ecologically practical and socio-politically acceptable management tool to consider in conservation strategies for spotted owls.

","language":"English","publisher":"Wildlife Society","doi":"10.1002/wmon.1009","usgsCitation":"Wiens, J.D., Anthony, R., and Forsman, E.D., 2014, Competitive interactions and resource partitioning between northern spotted owls and barred owls in western Oregon: Wildlife Monographs, v. 185, no. 1, p. 1-50, https://doi.org/10.1002/wmon.1009.","productDescription":"50 p.","startPage":"1","endPage":"50","numberOfPages":"50","temporalStart":"2007-01-01","temporalEnd":"2009-12-31","ipdsId":"IP-050049","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":282808,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Oregon Coast Ranges","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -123.9774,43.4807 ], [ -123.9774,44.0537 ], [ -123.1575,44.0537 ], [ -123.1575,43.4807 ], [ -123.9774,43.4807 ] ] ] } } ] }","volume":"185","issue":"1","noUsgsAuthors":false,"publicationDate":"2014-02-24","publicationStatus":"PW","scienceBaseUri":"578dfdafe4b0f1bea0e0f824","contributors":{"authors":[{"text":"Wiens, J. David","contributorId":9386,"corporation":false,"usgs":true,"family":"Wiens","given":"J.","email":"","middleInitial":"David","affiliations":[],"preferred":false,"id":490998,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Anthony, Robert G.","contributorId":61324,"corporation":false,"usgs":true,"family":"Anthony","given":"Robert G.","affiliations":[],"preferred":false,"id":490999,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Forsman, Eric D.","contributorId":96792,"corporation":false,"usgs":false,"family":"Forsman","given":"Eric","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":491000,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70160691,"text":"70160691 - 2014 - The effect of adjusting model inputs to achieve mass balance on time-dynamic simulations in a food-web model of Lake Huron","interactions":[],"lastModifiedDate":"2015-12-31T12:55:46","indexId":"70160691","displayToPublicDate":"2014-02-10T00:00:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"The effect of adjusting model inputs to achieve mass balance on time-dynamic simulations in a food-web model of Lake Huron","docAbstract":"

Ecopath with Ecosim (EwE) is a widely used modeling tool in fishery research and management. Ecopath requires a mass-balanced snapshot of a food web at a particular point in time, which Ecosim then uses to simulate changes in biomass over time. Initial inputs to Ecopath, including estimates for biomasses, production to biomass ratios, consumption to biomass ratios, and diets, rarely produce mass balance, and thus ad hoc changes to inputs are required to balance the model. There has been little previous research of whether ad hoc changes to achieve mass balance affect Ecosim simulations. We constructed an EwE model for the offshore community of Lake Huron, and balanced the model using four contrasting but realistic methods. The four balancing methods were based on two contrasting approaches; in the first approach, production of unbalanced groups was increased by increasing either biomass or the production to biomass ratio, while in the second approach, consumption of predators on unbalanced groups was decreased by decreasing either biomass or the consumption to biomass ratio. We compared six simulation scenarios based on three alternative assumptions about the extent to which mortality rates of prey can change in response to changes in predator biomass (i.e., vulnerabilities) under perturbations to either fishing mortality or environmental production. Changes in simulated biomass values over time were used in a principal components analysis to assess the comparative effect of balancing method, vulnerabilities, and perturbation types. Vulnerabilities explained the most variation in biomass, followed by the type of perturbation. Choice of balancing method explained little of the overall variation in biomass. Under scenarios where changes in predator biomass caused large changes in mortality rates of prey (i.e., high vulnerabilities), variation in biomass was greater than when changes in predator biomass caused only small changes in mortality rates of prey (i.e., low vulnerabilities), and was amplified when environmental production was increased. When standardized to mean changes in biomass within each scenario, scenarios when vulnerabilities were low and when fishing mortality was increased explained the most variation in biomass. Our findings suggested that approaches to balancing Ecopath models have relatively little effect on changes in biomass over time, especially when compared to assumptions about how mortality rates of prey change in response to changes in predator biomass. We concluded that when constructing food-web models using EwE, determining the effect of changes in predator biomass on mortality rates of prey should be prioritized over determining the best way to balance the model.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2013.10.027","collaboration":"Brian Langseth; Michael Jones","usgsCitation":"Langseth, B.J., Jones, M., and Riley, S.C., 2014, The effect of adjusting model inputs to achieve mass balance on time-dynamic simulations in a food-web model of Lake Huron: Ecological Modelling, v. 273, p. 44-54, https://doi.org/10.1016/j.ecolmodel.2013.10.027.","productDescription":"11 p.","startPage":"44","endPage":"54","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-049726","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":313147,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":312965,"type":{"id":15,"text":"Index Page"},"url":"https://dx.doi.org/10.1016/j.ecolmodel.2013.10.027"}],"country":"United States; Canada","otherGeospatial":"Lake Huron","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -83.75976562499999,\n 46.21785176740299\n ],\n [\n -83.91357421875,\n 46.00459325574482\n ],\n [\n -84.55078125,\n 45.99696161820381\n ],\n [\n -84.715576171875,\n 45.85176048817254\n ],\n [\n -84.649658203125,\n 45.73685954736049\n ],\n [\n -84.287109375,\n 45.65244828675087\n ],\n [\n -84.08935546875,\n 45.521743896993634\n ],\n [\n -83.507080078125,\n 45.35214524585177\n ],\n [\n -83.265380859375,\n 45.034714778688624\n ],\n [\n -83.3642578125,\n 45.058001435398296\n ],\n [\n -83.46313476562499,\n 44.98034238084973\n ],\n [\n -83.397216796875,\n 44.91035917458495\n ],\n [\n -83.33129882812499,\n 44.78573392716592\n ],\n [\n -83.3203125,\n 44.44162421758805\n ],\n [\n -83.49609375,\n 44.2294565683017\n ],\n [\n -83.726806640625,\n 44.01652134387754\n ],\n [\n -83.880615234375,\n 43.96119063892024\n ],\n [\n -83.95751953125,\n 43.77109381775651\n ],\n [\n -83.70483398437499,\n 43.59630591596548\n ],\n [\n -83.4521484375,\n 43.731414013769\n ],\n [\n -83.22143554687499,\n 43.96119063892024\n ],\n [\n -82.94677734375,\n 44.04811573082351\n ],\n [\n -82.694091796875,\n 43.937461690316646\n ],\n [\n -82.4853515625,\n 43.197167282501276\n ],\n [\n -82.41943359375,\n 42.96446257387128\n ],\n [\n -81.968994140625,\n 43.1811470593997\n ],\n [\n -81.705322265625,\n 43.42100882994726\n ],\n [\n -81.749267578125,\n 44.04811573082351\n ],\n [\n -81.595458984375,\n 44.33170718680922\n ],\n [\n -81.287841796875,\n 44.645208223744035\n ],\n [\n -81.67236328125,\n 45.259422036351694\n ],\n [\n -81.4306640625,\n 45.259422036351694\n ],\n [\n -81.298828125,\n 45.26715476332791\n ],\n [\n -81.2548828125,\n 45.0502402697946\n ],\n [\n -81.068115234375,\n 44.89479576469787\n ],\n [\n -80.936279296875,\n 44.77013681219717\n ],\n [\n -80.91430664062499,\n 44.61393394730626\n ],\n [\n -80.66162109375,\n 44.715513732021336\n ],\n [\n -80.17822265625,\n 44.52001001133986\n ],\n [\n -80.013427734375,\n 44.56699093657141\n ],\n [\n -80.123291015625,\n 44.75453548416007\n ],\n [\n -80.057373046875,\n 44.85586880735725\n ],\n [\n -79.82666015625,\n 44.77013681219717\n ],\n [\n -79.73876953125,\n 44.762336674810996\n ],\n [\n -80.079345703125,\n 45.120052841530516\n ],\n [\n -80.74951171875,\n 45.836454050187726\n ],\n [\n -81.375732421875,\n 45.98169518512228\n ],\n [\n -81.903076171875,\n 46.14178273759234\n ],\n [\n -82.85888671875,\n 46.20264638061019\n ],\n [\n -83.7158203125,\n 46.263442671779885\n ],\n [\n -83.75976562499999,\n 46.21785176740299\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"273","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"56865fcae4b0e7594ee74cd7","contributors":{"authors":[{"text":"Langseth, Brian J.","contributorId":60934,"corporation":false,"usgs":true,"family":"Langseth","given":"Brian","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":583571,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jones, Michael L.","contributorId":7219,"corporation":false,"usgs":false,"family":"Jones","given":"Michael L.","affiliations":[{"id":6590,"text":"Department of Fisheries and Wildlife, Michigan State University","active":true,"usgs":false}],"preferred":false,"id":583572,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Riley, Stephen C. 0000-0002-8968-8416 sriley@usgs.gov","orcid":"https://orcid.org/0000-0002-8968-8416","contributorId":2661,"corporation":false,"usgs":true,"family":"Riley","given":"Stephen","email":"sriley@usgs.gov","middleInitial":"C.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":false,"id":583570,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70099912,"text":"70099912 - 2014 - On the role of budget sufficiency, cost efficiency, and uncertainty in species management","interactions":[],"lastModifiedDate":"2018-01-05T10:04:20","indexId":"70099912","displayToPublicDate":"2014-02-01T14:24:42","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"On the role of budget sufficiency, cost efficiency, and uncertainty in species management","docAbstract":"Many conservation planning frameworks rely on the assumption that one should prioritize locations for management actions based on the highest predicted conservation value (i.e., abundance, occupancy). This strategy may underperform relative to the expected outcome if one is working with a limited budget or the predicted responses are uncertain. Yet, cost and tolerance to uncertainty rarely become part of species management plans. We used field data and predictive models to simulate a decision problem involving western burrowing owls (Athene cunicularia hypugaea) using prairie dog colonies (Cynomys ludovicianus) in western Nebraska. We considered 2 species management strategies: one maximized abundance and the other maximized abundance in a cost-efficient way. We then used heuristic decision algorithms to compare the 2 strategies in terms of how well they met a hypothetical conservation objective. Finally, we performed an info-gap decision analysis to determine how these strategies performed under different budget constraints and uncertainty about owl response. Our results suggested that when budgets were sufficient to manage all sites, the maximizing strategy was optimal and suggested investing more in expensive actions. This pattern persisted for restricted budgets up to approximately 50% of the sufficient budget. Below this budget, the cost-efficient strategy was optimal and suggested investing in cheaper actions. When uncertainty in the expected responses was introduced, the strategy that maximized abundance remained robust under a sufficient budget. Reducing the budget induced a slight trade-off between expected performance and robustness, which suggested that the most robust strategy depended both on one's budget and tolerance to uncertainty. Our results suggest that wildlife managers should explicitly account for budget limitations and be realistic about their expected levels of performance.","largerWorkType":{"id":2,"text":"Article"},"largerWorkTitle":"Journal of Wildlife Management","largerWorkSubtype":{"id":10,"text":"Journal Article"},"language":"English","publisher":"Wiley","doi":"10.1002/jwmg.638","usgsCitation":"van der Burg, M.P., Bly, B.B., Vercauteren, T., Grand, J.B., and Tyre, A.J., 2014, On the role of budget sufficiency, cost efficiency, and uncertainty in species management: Journal of Wildlife Management, v. 78, no. 1, p. 153-163, https://doi.org/10.1002/jwmg.638.","productDescription":"11 p.","startPage":"153","endPage":"163","ipdsId":"IP-041133","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":285063,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":285023,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1002/jwmg.638"}],"country":"United States","state":"Nebraska","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -104.0535,39.9999 ], [ -104.0535,43.0017 ], [ -95.3083,43.0017 ], [ -95.3083,39.9999 ], [ -104.0535,39.9999 ] ] ] } } ] }","volume":"78","issue":"1","noUsgsAuthors":false,"publicationDate":"2013-11-19","publicationStatus":"PW","scienceBaseUri":"53517059e4b05569d805a356","contributors":{"authors":[{"text":"van der Burg, Max Post","contributorId":92580,"corporation":false,"usgs":true,"family":"van der Burg","given":"Max","email":"","middleInitial":"Post","affiliations":[],"preferred":false,"id":492062,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bly, Bartholomew B.","contributorId":106011,"corporation":false,"usgs":true,"family":"Bly","given":"Bartholomew","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":492063,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vercauteren, Tammy","contributorId":23064,"corporation":false,"usgs":true,"family":"Vercauteren","given":"Tammy","email":"","affiliations":[],"preferred":false,"id":492061,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Grand, J. Barry 0000-0002-3576-4567 barry_grand@usgs.gov","orcid":"https://orcid.org/0000-0002-3576-4567","contributorId":579,"corporation":false,"usgs":true,"family":"Grand","given":"J.","email":"barry_grand@usgs.gov","middleInitial":"Barry","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":492059,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tyre, Andrew J.","contributorId":10720,"corporation":false,"usgs":true,"family":"Tyre","given":"Andrew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":492060,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70111900,"text":"70111900 - 2014 - Status of forest birds on Rota, Mariana Islands","interactions":[],"lastModifiedDate":"2014-07-07T11:00:13","indexId":"70111900","displayToPublicDate":"2014-02-01T10:52:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"seriesNumber":"TR HCSU-048","title":"Status of forest birds on Rota, Mariana Islands","docAbstract":"

The western Pacific island of Rota is the third largest human inhabited island in the Mariana archipelago, and is designated an Endemic Bird Area. Between 1982 and 2012, 12 point-transect distance sampling surveys were conducted to assess population status. Surveys did not consistently sample the entire island; thus, we used a ratio estimator to estimate bird abundances in strata not sampled during every survey. Occupancy models of the 2012 survey revealed general patterns of habitat use and detectability among 11 species that could be reliably modeled. The endangered Mariana crow (Corvus kubaryi) was dispersed around the periphery of the island in steep forested habitats. In contrast, the endangered Rota white-eye (Zosterops rotensis) was restricted to the high-elevation mesa. Precision of detection probabilities and occupancy estimates and effects of habitat types, sampling conditions, and specific observers varied considerably among species, indicating that more narrowly defined classifications and additional observer training may improve the accuracy of predictive modeling. Population estimates of five out of ten native bird species, including collared kingfisher (Todiramphus chloris orii), Mariana crow, Mariana fruit-dove (Ptilinopus roseicapilla), Micronesian myzomela (Myzomela rubrata), and white-throated ground-dove (Gallicolumba xanthonura) declined over the 30-year time series. The crow declined sharply to fewer than 200 individuals (upper 95% confidence interval). Trends increased for Micronesian starling (Aplonis opaca), rufous fantail (Rhipidura rufifrons mariae), and white tern (Gygis alba). Rota white-eye numbers declined from 1982 to the late 1990s, but returned to 1980s levels by 2012. The trend for the yellow bittern (Ixobrychus sinensis) was inconclusive. The alien Eurasian tree sparrow (Passer montanus) apparently increased in number despite an unreliable trend assessment. Declines were noted in the other two alien birds, black drongo (Dicrurus macrocercus) and island collared-dove (Streptopelia bitorquata). Total bird densities on Rota were similar to those on Saipan and Tinian, which were lower than densities on Aguiguan. Overall, bird trends on Rota declined, whereas trends observed for the same period on Saipan and Tinian were mixed, and trends on Aguiguan were stable to increasing. We identified several sampling design and protocol procedures that may improve the precision of occupancy, status, and trend assessments. Continued monitoring and demographic sampling are needed to understand why most bird species on Rota are declining, to identify the causative agents, and to assess effectiveness of conservation actions for rare species, especially the Mariana crow.

","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Hawaii Cooperative Studies Unit Technical Report","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"University of Hawaii","publisherLocation":"Hilo, HI","usgsCitation":"Camp, R., Brinck, K., Gorresen, P.M., Amidon, F.A., Radley, P.M., Berkowitz, S., and Banko, P.C., 2014, Status of forest birds on Rota, Mariana Islands, vi, 97 p.","productDescription":"vi, 97 p.","numberOfPages":"105","ipdsId":"IP-054916","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":289461,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":288193,"type":{"id":15,"text":"Index Page"},"url":"https://hilo.hawaii.edu/hcsu/publications.php"}],"country":"United States","otherGeospatial":"Marianas Islands","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ 145.120659,14.109592 ], [ 145.120659,14.201673 ], [ 145.2921,14.201673 ], [ 145.2921,14.109592 ], [ 145.120659,14.109592 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53bbc185e4b084059e8bff00","contributors":{"authors":[{"text":"Camp, Richard J.","contributorId":27392,"corporation":false,"usgs":true,"family":"Camp","given":"Richard J.","affiliations":[],"preferred":false,"id":494510,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brinck, Kevin W.","contributorId":78215,"corporation":false,"usgs":true,"family":"Brinck","given":"Kevin W.","affiliations":[],"preferred":false,"id":494513,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gorresen, P. Marcos mgorresen@usgs.gov","contributorId":37020,"corporation":false,"usgs":true,"family":"Gorresen","given":"P.","email":"mgorresen@usgs.gov","middleInitial":"Marcos","affiliations":[],"preferred":false,"id":494511,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Amidon, Fred A.","contributorId":107200,"corporation":false,"usgs":true,"family":"Amidon","given":"Fred","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":494514,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Radley, Paul M.","contributorId":7626,"corporation":false,"usgs":true,"family":"Radley","given":"Paul","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":494509,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Berkowitz, S. Paul","contributorId":44836,"corporation":false,"usgs":true,"family":"Berkowitz","given":"S. Paul","affiliations":[],"preferred":false,"id":494512,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Banko, Paul C. 0000-0002-6035-9803 pbanko@usgs.gov","orcid":"https://orcid.org/0000-0002-6035-9803","contributorId":3179,"corporation":false,"usgs":true,"family":"Banko","given":"Paul","email":"pbanko@usgs.gov","middleInitial":"C.","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true}],"preferred":true,"id":494508,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70144442,"text":"70144442 - 2014 - Coupled hydrological and biogeochemical processes controlling variability of nitrogen species in streamflow during autumn in an upland forest","interactions":[],"lastModifiedDate":"2015-03-30T15:20:16","indexId":"70144442","displayToPublicDate":"2014-02-01T00:00:00","publicationYear":"2014","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":"Coupled hydrological and biogeochemical processes controlling variability of nitrogen species in streamflow during autumn in an upland forest","docAbstract":"

Autumn is a season of dynamic change in forest streams of the northeastern United States due to effects of leaf fall on both hydrology and biogeochemistry. Few studies have explored how interactions of biogeochemical transformations, various nitrogen sources, and catchment flow paths affect stream nitrogen variation during autumn. To provide more information on this critical period, we studied (1) the timing, duration, and magnitude of changes to stream nitrate, dissolved organic nitrogen (DON), and ammonium concentrations; (2) changes in nitrate sources and cycling; and (3) source areas of the landscape that most influence stream nitrogen. We collected samples at higher temporal resolution for a longer duration than typical studies of stream nitrogen during autumn. This sampling scheme encompassed the patterns and extremes that occurred during base flow and stormflow events of autumn. Base flow nitrate concentrations decreased by an order of magnitude from 5.4 to 0.7 µmol L−1 during the week when most leaves fell from deciduous trees. Changes to rates of biogeochemical transformations during autumn base flow explained the low nitrate concentrations; in-stream transformations retained up to 72% of the nitrate that entered a stream reach. A decrease of in-stream nitrification coupled with heterotrophic nitrate cycling were primary factors in the seasonal nitrate decline. The period of low nitrate concentrations ended with a storm event in which stream nitrate concentrations increased by 25-fold. In the ensuing weeks, peak stormflow nitrate concentrations progressively decreased over closely spaced, yet similarly sized events. Most stormflow nitrate originated from nitrification in near-stream areas with occasional, large inputs of unprocessed atmospheric nitrate, which has rarely been reported for nonsnowmelt events. A maximum input of 33% unprocessed atmospheric nitrate to the stream occurred during one event. Large inputs of unprocessed atmospheric nitrate show direct and rapid effects on forest streams that may be widespread, although undocumented, throughout nitrogen-polluted temperate forests. In contrast to a week-long nitrate decline during peak autumn litterfall, base flow DON concentrations increased after leaf fall and remained high for 2 months. Dissolved organic nitrogen was hydrologically flushed to the stream from riparian soils during stormflow. In contrast to distinct seasonal changes in base flow nitrate and DON concentrations, ammonium concentrations were typically at or below the detection limit, similar to the rest of the year. Our findings reveal couplings among catchment flow paths, nutrient sources, and transformations that control seasonal extremes of stream nitrogen in forested landscapes.

","language":"English","publisher":"Wiley-Blackwell Publishing, Inc.","doi":"10.1002/2013WR013670","usgsCitation":"Sebestyen, S.D., Shanley, J.B., Boyer, E.W., Kendall, C., and Doctor, D.H., 2014, Coupled hydrological and biogeochemical processes controlling variability of nitrogen species in streamflow during autumn in an upland forest: Water Resources Research, v. 50, no. 2, p. 1569-1591, https://doi.org/10.1002/2013WR013670.","productDescription":"23 p.","startPage":"1569","endPage":"1591","numberOfPages":"23","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-051358","costCenters":[{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true}],"links":[{"id":473199,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/2013wr013670","text":"Publisher Index Page"},{"id":299159,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Vermont","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -72.13794708251953,\n 44.51878604321945\n ],\n [\n -72.22721099853516,\n 44.39625939021994\n ],\n [\n -72.16850280761719,\n 44.38521938054099\n ],\n [\n -72.17056274414062,\n 44.37196862007497\n ],\n [\n -72.09468841552734,\n 44.35773298166116\n ],\n [\n -72.04627990722656,\n 44.39895774251037\n ],\n [\n -72.08404541015625,\n 44.51070720877548\n ],\n [\n -72.13794708251953,\n 44.51878604321945\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"50","issue":"2","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationDate":"2014-02-24","publicationStatus":"PW","scienceBaseUri":"551a75cde4b0323842783502","contributors":{"authors":[{"text":"Sebestyen, Stephen D.","contributorId":107562,"corporation":false,"usgs":true,"family":"Sebestyen","given":"Stephen","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":543654,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Shanley, James B. 0000-0002-4234-3437 jshanley@usgs.gov","orcid":"https://orcid.org/0000-0002-4234-3437","contributorId":1953,"corporation":false,"usgs":true,"family":"Shanley","given":"James","email":"jshanley@usgs.gov","middleInitial":"B.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":543655,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Boyer, Elizabeth W.","contributorId":44659,"corporation":false,"usgs":false,"family":"Boyer","given":"Elizabeth","email":"","middleInitial":"W.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":543656,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kendall, Carol 0000-0002-0247-3405 ckendall@usgs.gov","orcid":"https://orcid.org/0000-0002-0247-3405","contributorId":1462,"corporation":false,"usgs":true,"family":"Kendall","given":"Carol","email":"ckendall@usgs.gov","affiliations":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":543657,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Doctor, Daniel H. 0000-0002-8338-9722 dhdoctor@usgs.gov","orcid":"https://orcid.org/0000-0002-8338-9722","contributorId":2037,"corporation":false,"usgs":true,"family":"Doctor","given":"Daniel","email":"dhdoctor@usgs.gov","middleInitial":"H.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":543658,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70058887,"text":"fs20133114 - 2014 - Landslides in the northern Colorado Front Range caused by rainfall, September 11-13, 2013","interactions":[],"lastModifiedDate":"2014-01-21T13:29:22","indexId":"fs20133114","displayToPublicDate":"2014-01-21T13:13:00","publicationYear":"2014","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2013-3114","title":"Landslides in the northern Colorado Front Range caused by rainfall, September 11-13, 2013","docAbstract":"During the second week of September 2013, nearly continuous rainfall caused widespread landslides and flooding in the northern Colorado Front Range. The combination of landslides and flooding was responsible for eight fatalities and caused extensive damage to buildings, highways, and infrastructure. Three fatalities were attributed to a fast moving type of landslide called debris flow. One fatality occurred in Jamestown, and two occurred in the community of Pinebrook Hills immediately west of the City of Boulder. All major canyon roads in the northern Front Range were periodically closed between September 11 and 13, 2013. Some canyon closures were caused by undercutting of roads by landslides and flooding, and some were caused by debris flows and rock slides that deposited material on road surfaces. Most of the canyon roads, with the exceptions of U.S. Highway 6 (Clear Creek Canyon), State Highway 46/Jefferson Co. Rd. 70 (Golden Gate Canyon), and Sunshine Canyon in Boulder County, remained closed at the end of September 2013. A review of historical records in Colorado indicates that this type of event, with widespread landslides and flooding occurring over a very large region, in such a short period of time, is rare.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20133114","usgsCitation":"Godt, J.W., Coe, J.A., Kean, J.W., Baum, R.L., Jones, E.S., Harp, E.L., Staley, D.M., and Barnhart, W.D., 2014, Landslides in the northern Colorado Front Range caused by rainfall, September 11-13, 2013: U.S. Geological Survey Fact Sheet 2013-3114, 3 p., https://doi.org/10.3133/fs20133114.","productDescription":"3 p.","numberOfPages":"3","ipdsId":"IP-052251","costCenters":[{"id":428,"text":"National Landslide Information Center","active":false,"usgs":true}],"links":[{"id":281326,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2013/3114/pdf/fs2013-3114.pdf"},{"id":281327,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/fs20133114.jpg"},{"id":281325,"type":{"id":15,"text":"Index Page"},"url":"https://pubs.usgs.gov/fs/2013/3114/"}],"country":"United States","state":"Colorado","county":"Boulder County","city":"Jamestown;Pinebrook Hills","otherGeospatial":"Clear Creek Canyon;Colorado Front Range;Golden Gate Canyon;Sunshine Canyon","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -105.8011,39.6797 ], [ -105.8011,40.7473 ], [ -105.0815,40.7473 ], [ -105.0815,39.6797 ], [ -105.8011,39.6797 ] ] ] } } ] }","noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"53cd640ee4b0b290850ff387","contributors":{"authors":[{"text":"Godt, Jonathan W. 0000-0002-8737-2493 jgodt@usgs.gov","orcid":"https://orcid.org/0000-0002-8737-2493","contributorId":1166,"corporation":false,"usgs":true,"family":"Godt","given":"Jonathan","email":"jgodt@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"preferred":true,"id":487415,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coe, Jeffrey A. 0000-0002-0842-9608 jcoe@usgs.gov","orcid":"https://orcid.org/0000-0002-0842-9608","contributorId":1333,"corporation":false,"usgs":true,"family":"Coe","given":"Jeffrey","email":"jcoe@usgs.gov","middleInitial":"A.","affiliations":[{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":487418,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kean, Jason W. 0000-0003-3089-0369 jwkean@usgs.gov","orcid":"https://orcid.org/0000-0003-3089-0369","contributorId":1654,"corporation":false,"usgs":true,"family":"Kean","given":"Jason","email":"jwkean@usgs.gov","middleInitial":"W.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":487419,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Baum, Rex L. 0000-0001-5337-1970 baum@usgs.gov","orcid":"https://orcid.org/0000-0001-5337-1970","contributorId":1288,"corporation":false,"usgs":true,"family":"Baum","given":"Rex","email":"baum@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":487416,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jones, Eric S. 0000-0002-9200-8442 esjones@usgs.gov","orcid":"https://orcid.org/0000-0002-9200-8442","contributorId":4924,"corporation":false,"usgs":true,"family":"Jones","given":"Eric","email":"esjones@usgs.gov","middleInitial":"S.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":487421,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Harp, Edwin L. harp@usgs.gov","contributorId":1290,"corporation":false,"usgs":true,"family":"Harp","given":"Edwin","email":"harp@usgs.gov","middleInitial":"L.","affiliations":[{"id":218,"text":"Denver Federal Center","active":false,"usgs":true}],"preferred":false,"id":487417,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Staley, Dennis M. 0000-0002-2239-3402 dstaley@usgs.gov","orcid":"https://orcid.org/0000-0002-2239-3402","contributorId":4134,"corporation":false,"usgs":true,"family":"Staley","given":"Dennis","email":"dstaley@usgs.gov","middleInitial":"M.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":487420,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Barnhart, William D. wbarnhart@usgs.gov","contributorId":5299,"corporation":false,"usgs":true,"family":"Barnhart","given":"William","email":"wbarnhart@usgs.gov","middleInitial":"D.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":487422,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70072613,"text":"70072613 - 2014 - Complexity versus certainty in understanding species’ declines","interactions":[],"lastModifiedDate":"2017-06-10T11:37:06","indexId":"70072613","displayToPublicDate":"2014-01-21T09:46:00","publicationYear":"2014","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1399,"text":"Diversity and Distributions","active":true,"publicationSubtype":{"id":10}},"title":"Complexity versus certainty in understanding species’ declines","docAbstract":"Aim\nOur understanding of and ability to predict species declines is limited, despite decades of study. We sought to expand our understanding of species declines within a regional landscape by testing models using both traditional hypotheses and those derived from a complex adaptive systems approach.\n\nLocation\nOur study area was the dry mixed grassland of south-eastern Alberta, Canada, one of the largest remnants of native grassland in North America, and the adjacent grassland in Saskatchewan.\n\nMethods\nWe used the breeding birds of the grassland to test the relationship between species declines and a suite of traits associated with decline (such as size, specialization and rarity, as well as distance to edge of a discontinuity, and edge of geographic range) in a stepwise regression with AICc values and bootstrapping via model averaging, followed by a refit procedure to obtain model-averaged parameter estimates. We used both provincial government and Breeding Bird Survey (BBS) classifications of decline. We also modelled degree of decline in the Alberta and Saskatchewan grasslands, which differ in amount of habitat remaining, to test whether severity of decline was explained by the same traits as species decline/not- decline.\n\nResults\nWe found that the model for government-defined decline fulfilled government expectations that species' extinction risk is a function of being large, specialized, rare and carnivorous, whereas the model for BBS-defined decline suggested that the biological reality of decline is more complex, requiring the need to explicitly model scale-specific patterns. Furthermore, species decline/not- decline was explained by different traits than those that fit degree of decline, though complex systems- derived traits featured in both sets of models.\n\nMain conclusions\nTraditional approaches to predict species declines (e.g. government processes or IUCN Red Lists), may be too simplistic and may therefore misguide management and conservation. Using complex systems approaches that account for scale-specific patterns and processes have the potential to overcome these limitations.","language":"English","publisher":"Wiley","doi":"10.1111/ddi.12166","usgsCitation":"Sundstrom, S.M., and Allen, C.R., 2014, Complexity versus certainty in understanding species’ declines: Diversity and Distributions, v. 3, p. 344-355, https://doi.org/10.1111/ddi.12166.","productDescription":"12 p.","startPage":"344","endPage":"355","ipdsId":"IP-052551","costCenters":[],"links":[{"id":281307,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":281306,"type":{"id":10,"text":"Digital Object Identifier"},"url":"https://dx.doi.org/10.1111/ddi.12166"}],"volume":"3","edition":"20","noUsgsAuthors":false,"publicationDate":"2014-01-03","publicationStatus":"PW","scienceBaseUri":"52df97f6e4b0d7b3a14e1a9b","contributors":{"authors":[{"text":"Sundstrom, Shana M.","contributorId":7159,"corporation":false,"usgs":true,"family":"Sundstrom","given":"Shana","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":488527,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Allen, Craig R. 0000-0001-8655-8272 allencr@usgs.gov","orcid":"https://orcid.org/0000-0001-8655-8272","contributorId":1979,"corporation":false,"usgs":true,"family":"Allen","given":"Craig","email":"allencr@usgs.gov","middleInitial":"R.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":488526,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ]}