{"pageNumber":"377","pageRowStart":"9400","pageSize":"25","recordCount":184776,"records":[{"id":70262179,"text":"70262179 - 2022 - A comparison of non-surgical methods for sexing young gopher tortoises (Gopherus polyphemus)","interactions":[],"lastModifiedDate":"2025-01-16T14:35:18.64688","indexId":"70262179","displayToPublicDate":"2022-06-14T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3840,"text":"PeerJ","active":true,"publicationSubtype":{"id":10}},"title":"A comparison of non-surgical methods for sexing young gopher tortoises (Gopherus polyphemus)","docAbstract":"

Many turtle species have temperature-dependent sex determination (TSD), raising the prospect that climate change could impact population dynamics by altering sex ratios. Understanding how climate change will affect populations of animals with TSD requires a reliable and minimally invasive method of identifying the sexes of young individuals. This determination is challenging in many turtles, which often lack conspicuous external sexual dimorphism until years after hatching. Here, we explore four alternatives for sexing three age classes of captive-reared young gopher tortoises (Gopherus polyphemus), a terrestrial turtle of conservation concern native to the southeastern United States: (1) naive testosterone levels, (2) testosterone levels following a follicle stimulating hormone (FSH) challenge, (3) linear morphological measurements, and (4) geometric morphometrics. Unlike some other turtle species, male and female neonatal gopher tortoises have overlapping naive testosterone concentration distributions, justifying more complicated methods. We found that sex of neonates (<7 days old) is best predicted by a “random forest” machine learning model with naive testosterone levels and morphological measurements (8% out-of-bag error). Sex of hatchlings (4–8 months old) was predicted with 11% error using a simple threshold on naive testosterone levels, or with 4% error using a simple threshold on post-FSH testosterone levels. Sex of juveniles (approximately 3.5 years old) was perfectly predicted using a simple threshold on naive testosterone levels. Sexing hatchlings at >4 months of age is the easiest and most reliable non-surgical method for sex identification. Given access to a rearing facility and equipment to perform hormone assays, these methods have the potential to supplant laparoscopic surgery as the method of choice for sexing young gopher tortoises.

","language":"English","publisher":"PeerJ Inc","doi":"10.7717/peerj.13599","usgsCitation":"Loope, K., Rostal, D., Walden, M., Shoemaker, K., and Hunter, E.A., 2022, A comparison of non-surgical methods for sexing young gopher tortoises (Gopherus polyphemus): PeerJ, v. 10, e13599, 21 p., https://doi.org/10.7717/peerj.13599.","productDescription":"e13599, 21 p.","ipdsId":"IP-136848","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":467180,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.13599","text":"Publisher Index Page"},{"id":466447,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"id\":11,\"properties\":{\"name\":\"Georgia\",\"nation\":\"USA 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Kevin J.","contributorId":348329,"corporation":false,"usgs":false,"family":"Loope","given":"Kevin J.","affiliations":[{"id":16976,"text":"Georgia Southern University","active":true,"usgs":false}],"preferred":false,"id":923369,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rostal, David C.","contributorId":348330,"corporation":false,"usgs":false,"family":"Rostal","given":"David C.","affiliations":[{"id":16976,"text":"Georgia Southern University","active":true,"usgs":false}],"preferred":false,"id":923370,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walden, Margarete A.","contributorId":348331,"corporation":false,"usgs":false,"family":"Walden","given":"Margarete A.","affiliations":[{"id":83335,"text":"University of Nevada–Reno","active":true,"usgs":false}],"preferred":false,"id":923371,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Shoemaker, Kevin T.","contributorId":348332,"corporation":false,"usgs":false,"family":"Shoemaker","given":"Kevin T.","affiliations":[{"id":83335,"text":"University of Nevada–Reno","active":true,"usgs":false}],"preferred":false,"id":923372,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hunter, Elizabeth Ann 0000-0003-4710-167X","orcid":"https://orcid.org/0000-0003-4710-167X","contributorId":288535,"corporation":false,"usgs":true,"family":"Hunter","given":"Elizabeth","email":"","middleInitial":"Ann","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923373,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70232205,"text":"70232205 - 2022 - A haploid pseudo-chromosome genome assembly for a keystone sagebrush species of western North American rangelands","interactions":[],"lastModifiedDate":"2022-07-08T13:51:45.839294","indexId":"70232205","displayToPublicDate":"2022-06-13T11:17:41","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":10934,"text":"G3 Genes, Genomes, Genetics","active":true,"publicationSubtype":{"id":10}},"title":"A haploid pseudo-chromosome genome assembly for a keystone sagebrush species of western North American rangelands","docAbstract":"

Increased ecological disturbances, species invasions, and climate change are creating severe conservation problems for several plant species that are widespread and foundational. Understanding the genetic diversity of these species and how it relates to adaptation to these stressors are necessary for guiding conservation and restoration efforts. This need is particularly acute for big sagebrush (Artemisia tridentata; Asteraceae), which was once the dominant shrub over 1,000,000 km2 in western North America but has since retracted by half and thus has become the target of one of the largest restoration seeding efforts globally. Here, we present the first reference-quality genome assembly for an ecologically important subspecies of big sagebrush (A. tridentata subsp. tridentata) based on short and long reads, as well as chromatin proximity ligation data analyzed using the HiRise pipeline. The final 4.2-Gb assembly consists of 5,492 scaffolds, with nine pseudo-chromosomal scaffolds (nine scaffolds comprising at least 90% of the assembled genome; n = 9). The assembly contains an estimated 43,377 genes based on ab initio gene discovery and transcriptional data analyzed using the MAKER pipeline, with 91.37% of BUSCOs being completely assembled. The final assembly was highly repetitive, with repeat elements comprising 77.99% of the genome, making the Artemisia tridentata subsp. tridentata genome one of the most highly repetitive plant genomes to be sequenced and assembled. This genome assembly advances studies on plant adaptation to drought and heat stress and provides a valuable tool for future genomic research.

","language":"English","publisher":"Oxford University Press","doi":"10.1093/g3journal/jkac122","usgsCitation":"Melton, A.E., Child, A.W., Beard, R.S., Dumaguit, C.D., Forbey, J.S., Germino, M., de Graaff, M., Kliskey, A., Leitch, I.J., Martinez, P., Novak, S.J., Pellicer, J., Richardson, B., Self, D., Serpe, M.D., and Buerki, S., 2022, A haploid pseudo-chromosome genome assembly for a keystone sagebrush species of western North American rangelands: G3 Genes, Genomes, Genetics, v. 12, no. 7, jkac122, 9 p., https://doi.org/10.1093/g3journal/jkac122.","productDescription":"jkac122, 9 p.","ipdsId":"IP-138937","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":447434,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/g3journal/jkac122","text":"Publisher Index Page"},{"id":402105,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, California, Colorado, Idaho, Montana, Nebraska, Nevada, New Mexico, North Dakota, Oregon, South Dakota, Utah, Washington, Wyoming","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.28906250000001,\n 35.817813158696616\n ],\n [\n -103.3154296875,\n 35.817813158696616\n ],\n [\n -103.3154296875,\n 48.980216985374994\n ],\n [\n -121.28906250000001,\n 48.980216985374994\n ],\n [\n -121.28906250000001,\n 35.817813158696616\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"7","noUsgsAuthors":false,"publicationDate":"2022-05-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Melton, Anthony E.","contributorId":292452,"corporation":false,"usgs":false,"family":"Melton","given":"Anthony","email":"","middleInitial":"E.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":844626,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Child, Andrew W.","contributorId":292453,"corporation":false,"usgs":false,"family":"Child","given":"Andrew","email":"","middleInitial":"W.","affiliations":[{"id":6711,"text":"University of Idaho, Moscow ID","active":true,"usgs":false}],"preferred":false,"id":844627,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beard, Richard S. Jr.","contributorId":292454,"corporation":false,"usgs":false,"family":"Beard","given":"Richard","suffix":"Jr.","email":"","middleInitial":"S.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":844628,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dumaguit, Carlos Dave C.","contributorId":292455,"corporation":false,"usgs":false,"family":"Dumaguit","given":"Carlos","email":"","middleInitial":"Dave C.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":844629,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Forbey, Jennifer S.","contributorId":194442,"corporation":false,"usgs":false,"family":"Forbey","given":"Jennifer","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":844630,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Germino, Matthew J. 0000-0001-6326-7579","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":251901,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":844603,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"de Graaff, Marie-Anne","contributorId":195121,"corporation":false,"usgs":false,"family":"de Graaff","given":"Marie-Anne","email":"","affiliations":[],"preferred":false,"id":844631,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Kliskey, Andrew","contributorId":189256,"corporation":false,"usgs":false,"family":"Kliskey","given":"Andrew","email":"","affiliations":[],"preferred":false,"id":844632,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Leitch, Ilia J.","contributorId":292456,"corporation":false,"usgs":false,"family":"Leitch","given":"Ilia","email":"","middleInitial":"J.","affiliations":[{"id":48590,"text":"Royal Botanic Gardens","active":true,"usgs":false}],"preferred":false,"id":844633,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Martinez, Peggy","contributorId":292457,"corporation":false,"usgs":false,"family":"Martinez","given":"Peggy","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":844634,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Novak, Stephen J.","contributorId":208249,"corporation":false,"usgs":false,"family":"Novak","given":"Stephen","email":"","middleInitial":"J.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":844635,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Pellicer, Jaume","contributorId":292458,"corporation":false,"usgs":false,"family":"Pellicer","given":"Jaume","email":"","affiliations":[{"id":48590,"text":"Royal Botanic Gardens","active":true,"usgs":false}],"preferred":false,"id":844636,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Richardson, Bryce A.","contributorId":37249,"corporation":false,"usgs":true,"family":"Richardson","given":"Bryce A.","affiliations":[],"preferred":false,"id":844637,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Self, Desiree","contributorId":292461,"corporation":false,"usgs":false,"family":"Self","given":"Desiree","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":844638,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Serpe, Marcelo D.","contributorId":257074,"corporation":false,"usgs":false,"family":"Serpe","given":"Marcelo","email":"","middleInitial":"D.","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":844639,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Buerki, Sven","contributorId":257075,"corporation":false,"usgs":false,"family":"Buerki","given":"Sven","email":"","affiliations":[{"id":16201,"text":"Boise State University","active":true,"usgs":false}],"preferred":false,"id":844640,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70232202,"text":"70232202 - 2022 - How do accuracy and model agreement vary with versioning, scale, and landscape heterogeneity for satellite-derived vegetation maps in sagebrush steppe?","interactions":[],"lastModifiedDate":"2022-06-14T13:08:00.238472","indexId":"70232202","displayToPublicDate":"2022-06-13T11:00:15","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"How do accuracy and model agreement vary with versioning, scale, and landscape heterogeneity for satellite-derived vegetation maps in sagebrush steppe?","docAbstract":"

Maps of the distribution and abundance of dominant plants derived from satellite data are essential for ecological research and management, particularly in the vast semiarid shrub-steppe. Appropriate application of these maps requires an understanding of model accuracy and precision, and how it might vary across space, time, and different vegetation types. For a 113 k Ha burn area, we compared modeled maps of different vegetation cover types created from satellite data to ‘benchmark” models based on intensive field sampling (~1500-2000 plots resampled annually for 5 years) for three new satellite-derived models: USDA Rangeland Analysis Platform (RAP), the USGS Rangeland Condition Monitoring Assessment and Projection (RCMAP), and USGS fractional estimate of exotic annual grass cover (USGS-fractional-EAG). We assessed out-of-sample point accuracy and asked if and how accuracy changed each year due to vegetation shifts, new images, and model improvements (i.e. model versions). We also assessed how map agreement between satellite-based and field-based models changed with scale of application, topography, and time since fire.

Accuracy and map agreement varied considerably among the vegetation types and across time and space (r2 ranging from 0 to 0.53), and some of the variability was predictable. All models tended to over or underestimate cover when field-measured cover was relatively low or high, respectively, i.e. a “false moderating effect”. Accuracy was greater and improved with newer versions of RAP (+0.05 to 0.29 r2) compared to RCMAP and USGS fractional model estimates, and in some cases was greater than field-based models. Variability in map agreement tended to decrease with larger areas sampled (particularly in areas >12 km), and this scale dependency was more evident in RAP and USGS-fractional-EAG models. Creating a “fair” basis for comparison of spatial models of low-statured semiarid vegetation derived from satellite compared to field data is not trivial because scaling the field data to the scale of large satellite pixels (or downscaling satellite-based models to field scale) requires modeling and associated model uncertainty. Accuracy can vary considerably and understanding the variation can help guide application of the models to the appropriate time, place, and variables.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2022.108935","usgsCitation":"Applestein, C., and Germino, M., 2022, How do accuracy and model agreement vary with versioning, scale, and landscape heterogeneity for satellite-derived vegetation maps in sagebrush steppe?: Ecological Indicators, v. 139, 108935, 11 p., https://doi.org/10.1016/j.ecolind.2022.108935.","productDescription":"108935, 11 p.","ipdsId":"IP-138386","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":447436,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2022.108935","text":"Publisher Index Page"},{"id":435803,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9P8G8XM","text":"USGS data release","linkHelpText":"Modelled functional group vegetation cover from 2016 to 2020 on the Soda Wildfire"},{"id":402095,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Oregon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119.05883789062501,\n 42.391008609205045\n ],\n [\n -114.730224609375,\n 42.391008609205045\n ],\n [\n -114.730224609375,\n 45.44471679159555\n ],\n [\n -119.05883789062501,\n 45.44471679159555\n ],\n [\n -119.05883789062501,\n 42.391008609205045\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"139","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Applestein, Cara 0000-0002-7923-8526","orcid":"https://orcid.org/0000-0002-7923-8526","contributorId":205748,"corporation":false,"usgs":true,"family":"Applestein","given":"Cara","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":844582,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Germino, Matthew J. 0000-0001-6326-7579 mgermino@usgs.gov","orcid":"https://orcid.org/0000-0001-6326-7579","contributorId":152582,"corporation":false,"usgs":true,"family":"Germino","given":"Matthew J.","email":"mgermino@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":844583,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70232200,"text":"70232200 - 2022 - Thirteen novel ideas and underutilized resources to support progress towards a range-wide American eel stock assessment","interactions":[],"lastModifiedDate":"2022-09-15T14:08:12.517171","indexId":"70232200","displayToPublicDate":"2022-06-13T10:49:48","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1659,"text":"Fisheries Management and Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Thirteen novel ideas and underutilized resources to support progress towards a range-wide American eel stock assessment","docAbstract":"

A robust assessment of the American eel (Anguilla rostrata) stock, required to guide conservation efforts, is challenged by the species’ vast range, high variability in demographic parameters and data inadequacies. Novel ideas and underutilised resources that may assist both analytic assessments and spatially oriented modelling include (1) species and environmental databases; (2) mining of data from scattered sources; (3) infilling of data gaps by spatial analysis; (4) age estimation from measurements of DNA methylation; evaluation of eel abundance by (5) larval, (6) glass-bottom boat, (7) net enclosure and (8) eDNA surveys; (9) accounting for dam-induced habitat increases in eel watercourse modelling; (10) spatially oriented modelling with and without temporal components; (11) geographically nested modelling of glass eel recruitment; (12) spawner per recruit modelling and (13) life cycle modelling to examine larval allocation effects. Eel biologists are too few to gather the required assessment data across all of the species’ range. Public posting of electrofishing and eDNA metabarcoding data sets and the use of machine learning techniques to comprehensively inventory small dams will help meet some data needs. These approaches address only a small proportion of the assessment challenges that face American eels. Worldwide collaboration amongst Anguilla scientists is a key enabler of progress towards stock assessment goals.

","language":"English","publisher":"Wiley","doi":"10.1111/fme.12572","usgsCitation":"Cairns, D., Benchetrit, J., Bernatchez, L., Bornarel, V., Casselman, J., Castonguay, M., Charsley, A., Dorrow, M., Drouineau, H., Frankowski, J., Haro, A., Hoyle, S., Knickle, D.C., Koops, M.A., Poirier, L.A., Thorson, J.T., Young, J.A., and Zhu, X., 2022, Thirteen novel ideas and underutilized resources to support progress towards a range-wide American eel stock assessment: Fisheries Management and Ecology, v. 29, no. 5, p. 516-541, https://doi.org/10.1111/fme.12572.","productDescription":"26 p.","startPage":"516","endPage":"541","ipdsId":"IP-135604","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":447439,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/fme.12572","text":"Publisher Index 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Best subsets regression analysis was used to develop linear regression models that quantify relations among five categories of hydrologic explanatory metrics (i.e., duration, frequency, magnitude, rate-of-change, and timing) computed from streamgage records and six categories of biological response metrics (i.e., composition, habitat, life history, reproductive, tolerance, trophic) computed from fish community samples, as well as fish-based indices of biotic integrity (FIBI) scores and FIBI scores normalized to the an impairment threshold of the corresponding stream class (FIBI_BCG4). Three hydrologic datasets were used to examine rRelations between altered hydrology and fish community responses were examined at three different temporal scalesusing three hydrologic datasets that represented periods of record, long-term changes, and short-term changes to flow regimes in streams of Minnesota.","language":"English","publisher":"Springer","doi":"10.1186/s13717-022-00383-z","usgsCitation":"Ziegeweid, J.R., Johnson, G.D., Krall, A.L., Fitzpatrick, K., and Levin, S., 2022, Quantifying relations between altered hydrology and fish community responses for streams in Minnesota: Ecological Processes, v. 11, 41, 25 p., https://doi.org/10.1186/s13717-022-00383-z.","productDescription":"41, 25 p.","ipdsId":"IP-125704","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":447443,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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B.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":844555,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70232198,"text":"70232198 - 2022 - Dammed water quality — Longitudinal stream responses below beaver ponds in the Umpqua River Basin, Oregon","interactions":[],"lastModifiedDate":"2022-07-08T13:50:20.60167","indexId":"70232198","displayToPublicDate":"2022-06-13T10:43:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1447,"text":"Ecohydrology","active":true,"publicationSubtype":{"id":10}},"title":"Dammed water quality — Longitudinal stream responses below beaver ponds in the Umpqua River Basin, Oregon","docAbstract":"

Beaver-related restoration (BRR) has gained popularity as a means of improving stream ecosystems, but the effects are not fully understood. Studies of dissolved oxygen (DO) and water temperature, key water quality metrics for salmonids, have demonstrated improved conditions in some cases, but warming and decreased DO have been more commonly reported in meta-analyses. These results point to the contingencies that can influence outcomes from BRR. We examined water quality related to beaver ponds in a diverse coastal watershed (Umpqua River Basin, OR, USA). We monitored water temperature 0–400 m above and below beaver ponds and at pond surfaces and bottoms across seven study sites from June through September of 2019. DO was also recorded at two sites at pond surfaces and pond bottoms. Downstream monthly mean daily maximum temperatures were warmer than upstream reference locations by up to 1.9°C at beaver dam outlets but this heating signal attenuated with downstream distance. Downstream warming was greatest in June and July and best predicted by pond bottom temperatures. DO at pond surfaces and bottoms were hypoxic (≤5 mg/L) for more than half of the 32-day monitoring period. Water temperatures increased for short distances below monitored beaver ponds and observed oxygen conditions within ponds were largely unsuitable for salmonid fishes. These findings contrast with some commonly stated expectations of BRR, and we recommend that managers consider these expectations prior to implementation. In some cases, project goals may override water quality concerns but in streams where temperature or DO restoration are objectives, managers may consider using BRR techniques with caution.

","language":"English","publisher":"Wiley","doi":"10.1002/eco.2430","usgsCitation":"Stevenson, J.R., Dunham, J.B., Wondzell, S., and Taylor, J.D., 2022, Dammed water quality — Longitudinal stream responses below beaver ponds in the Umpqua River Basin, Oregon: Ecohydrology, v. 15, no. 4, e2430, 16 p., https://doi.org/10.1002/eco.2430.","productDescription":"e2430, 16 p.","ipdsId":"IP-134551","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":402093,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Umpqua River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.21005249023438,\n 43.6599240747891\n ],\n [\n -124.09469604492186,\n 43.60923380393403\n ],\n [\n -123.92715454101562,\n 43.520671902437606\n ],\n [\n -123.81866455078125,\n 43.38608793041562\n ],\n [\n -123.90106201171875,\n 43.24420236973\n ],\n [\n -123.59619140625001,\n 43.111009147075116\n ],\n [\n -122.72003173828124,\n 43.159112387154174\n ],\n [\n -122.61154174804686,\n 43.206176810164784\n ],\n [\n -122.34374999999999,\n 43.345154990451135\n ],\n [\n -122.32177734375,\n 43.560491112629286\n ],\n [\n -122.36572265625,\n 43.82660134505382\n ],\n [\n -122.72277832031251,\n 44.134913443750726\n ],\n [\n -123.0743408203125,\n 44.25306865928177\n ],\n [\n -123.43688964843749,\n 44.26093725039923\n ],\n [\n -123.77197265625,\n 44.166444664458595\n ],\n [\n -124.09057617187499,\n 44.044167353572185\n ],\n [\n -124.21005249023438,\n 43.6599240747891\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"15","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-06-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Stevenson, John R.","contributorId":147936,"corporation":false,"usgs":false,"family":"Stevenson","given":"John","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":844547,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dunham, Jason B. 0000-0002-6268-0633 jdunham@usgs.gov","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":147808,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","email":"jdunham@usgs.gov","middleInitial":"B.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":844548,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wondzell, Steve M.","contributorId":236920,"corporation":false,"usgs":false,"family":"Wondzell","given":"Steve M.","affiliations":[{"id":7134,"text":"USFS","active":true,"usgs":false}],"preferred":false,"id":844549,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Taylor, Jimmy D.","contributorId":140178,"corporation":false,"usgs":false,"family":"Taylor","given":"Jimmy","email":"","middleInitial":"D.","affiliations":[{"id":13402,"text":"USDA APHIS Wildlife Services","active":true,"usgs":false}],"preferred":false,"id":844550,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70232196,"text":"70232196 - 2022 - Spectral mixture analysis for surveillance of harmful algal blooms (SMASH): A field-, laboratory-, and satellite-based approach to identifying cyanobacteria genera from remotely sensed data","interactions":[],"lastModifiedDate":"2022-06-13T15:44:24.981546","indexId":"70232196","displayToPublicDate":"2022-06-13T10:31:26","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Spectral mixture analysis for surveillance of harmful algal blooms (SMASH): A field-, laboratory-, and satellite-based approach to identifying cyanobacteria genera from remotely sensed data","docAbstract":"

Algal blooms around the world are increasing in frequency and severity, often with the possibility of adverse effects on human and ecosystem health. The health and economic impacts associated with harmful algal blooms, or HABs, provide compelling rationale for developing new methods for monitoring these events via remote sensing. Although concentrations of chlorophyll-a and key pigments like phycocyanin are routinely estimated from satellite images and used to infer algal or cyanobacterial cell counts, current methods are unable to provide information on the taxonomic composition of a bloom. This study introduced a new approach capable of differentiating among genera based on their reflectance characteristics: Spectral Mixture Analysis for Surveillance of HABs, or SMASH. The foundation of SMASH is a multiple endmember spectral mixture analysis (MESMA) algorithm that takes a library of cyanobacteria endmembers and a hyperspectral image as input and estimates the fractional abundance of each genus, plus water, on a per-pixel basis. Importantly, we assume that the water column consists of only pure water and cyanobacteria, implying that our linear spectral unmixing models do not account for other optically active constituents such as suspended sediment and colored dissolved organic matter (CDOM). We used reflectance spectra for 12 genera measured under a microscope to populate an algal spectral library and applied the SMASH workflow to satellite images from four waterbodies across the United States. Normalized spectral separability scores indicated that the 12 genera were distinct from one another and the MESMA algorithm reproduced known input fractions for simulated mixtures that included all pairwise combinations of genera and water. We used Upper Klamath Lake as an example to illustrate data products generated via SMASH: maps of the normalized difference chlorophyll index and cyanobacterial index, a MESMA-based classification of algal genera, fraction images for each endmember, and a root mean square error (RMSE) image that summarizes uncertainty. For Upper Klamath Lake, these outputs highlighted a complex algal bloom featuring several genera, primarily Aphanizomenon, and intricate spatial patterns associated with gyres. The maximum RMSE constraint imposed on the MESMA algorithm provided a means of avoiding false positive detection of genera not present in a waterbody but must not be set so low as to leave much of an image unclassified in cases where genera included in the library are present. Comparison of endmember fractions with relative biovolumes calculated from field samples indicated that taxonomic information from SMASH was consistent with field observations. For example, the algorithm successfully identified Microcystis in Owasco Lake but avoided misclassifying Asterionella, a genus not yet included in our library, in Detroit Lake. This proof-of-concept investigation demonstrates the potential of SMASH to enhance our understanding of algal blooms, particularly with respect to their spatial and temporal dynamics.


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Growing interest in forests as relatively cost-effective nature-based climate solutions, particularly restoration and reforestation activities, has increased the demand for information on forest regrowth and recovery following natural and anthropogenic disturbances (e.g., fire, harvest, or thinning). However, a wall-to-wall mapping of the CONUS tree regrowth duration at an annual time interval and 30-m resolution is still challenging. In this study, we utilized the annual land cover products to develop a dataset to quantify forest regrowth duration for CONUS over 1985–2017. The land cover data used to derive the tree regrowth duration map is from the primary land cover product in the U.S. Geological Survey’s Land Change Monitoring, Assessment, and Projection (LCMAP) collection. The LCMAP product used all available Landsat images to detect disturbances over forest and classify Grass/Shrub to Tree Cover transitions on an annual basis. The average regrowth duration was then calculated for each pixel. The regrowth duration map was validated using human interpreted annual reference data that were collected independently. The validation results show one-year of underestimation and 6-year standard deviation of error between the reference data and regrowth duration map. In southeastern CONUS, where major tree regrowth activities have been observed, our map showed higher accuracy with less than one-year bias and 3.6 years standard deviation of error. Forest in the southeast took around 5 years to recover, which was faster than other regions of CONUS. Many pixels had multiple disturbances during the 33-year study period in the region. The spatial pattern of the tree regrowth indicated intense harvesting activities in this region. The Pacific Northwest coast region was the second main area of tree regrowth, but this region often took multiple decades to recover. Given increasing interest in forests as nature-based climate solutions, the tree regrowth duration map can be used to assess reforestation activities as well as forest recovery following natural disturbance and harvesting.","language":"English","publisher":"Taylor & Francis","doi":"10.1080/15481603.2022.2083790","usgsCitation":"Zhou, Q., Xian, G.Z., Horton, J., Wellington, D., Domke, G., Auch, R.F., Li, C., and Zhu, Z., 2022, Tree regrowth duration map from LCMAP collection 1.0 land cover products in the conterminous United States, 1985–2017: GIScience & Remote Sensing, v. 59, no. 1, p. 959-974, https://doi.org/10.1080/15481603.2022.2083790.","productDescription":"16 p.","startPage":"959","endPage":"974","ipdsId":"IP-131173","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":37273,"text":"Advanced Research Computing (ARC)","active":true,"usgs":true}],"links":[{"id":447450,"rank":2,"type":{"id":40,"text":"Open Access 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]\n}","volume":"59","issue":"1","noUsgsAuthors":false,"publicationDate":"2022-06-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Zhou, Qiang 0000-0002-1282-8177","orcid":"https://orcid.org/0000-0002-1282-8177","contributorId":265886,"corporation":false,"usgs":false,"family":"Zhou","given":"Qiang","affiliations":[{"id":54817,"text":"AFDS, contractor to U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":844650,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Xian, George Z. 0000-0001-5674-2204","orcid":"https://orcid.org/0000-0001-5674-2204","contributorId":238919,"corporation":false,"usgs":true,"family":"Xian","given":"George","email":"","middleInitial":"Z.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":844651,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Horton, Josephine 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Service","active":true,"usgs":false}],"preferred":false,"id":844654,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Auch, Roger F. 0000-0002-5382-5044 auch@usgs.gov","orcid":"https://orcid.org/0000-0002-5382-5044","contributorId":667,"corporation":false,"usgs":true,"family":"Auch","given":"Roger","email":"auch@usgs.gov","middleInitial":"F.","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true},{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":844655,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Li, Congcong 0000-0002-4311-4169","orcid":"https://orcid.org/0000-0002-4311-4169","contributorId":270142,"corporation":false,"usgs":false,"family":"Li","given":"Congcong","email":"","affiliations":[{"id":52693,"text":"ASRC Federal","active":true,"usgs":false}],"preferred":false,"id":844656,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Zhu, Zhe 0000-0001-8283-6407","orcid":"https://orcid.org/0000-0001-8283-6407","contributorId":190828,"corporation":false,"usgs":false,"family":"Zhu","given":"Zhe","affiliations":[],"preferred":false,"id":844657,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70247395,"text":"70247395 - 2022 - The role of microtopography and resident species in post-disturbance recovery of arid habitats in Hawaiʻi","interactions":[],"lastModifiedDate":"2023-08-02T14:13:17.608115","indexId":"70247395","displayToPublicDate":"2022-06-13T08:55:52","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"The role of microtopography and resident species in post-disturbance recovery of arid habitats in Hawaiʻi","docAbstract":"

Habitat-suitability indices (HSI) have been employed in restoration to identify optimal sites for planting native species. Often, HSI are based on abiotic variables and do not include biotic interactions, even though similar abiotic conditions can favor both native and nonnative species. Biotic interactions such as competition may be especially important in invader-dominated habitats because invasive species often have fast growth rates and can exploit resources quickly. In this study, we test the utility of an HSI of microtopography derived from airborne LiDAR to predict post-disturbance recovery and native planting success in native shrub-dominated and nonnative, invasive grass-dominated dryland habitats in Hawaiʻi. The HSI uses high-resolution digital terrain models to classify sites' microtopography as high, medium, or low suitability, based on wind exposure and topographic position. We used a split-plot before-after-control-impact design to implement a disturbance experiment within native shrub (Dodonaea viscosa) and nonnative, invasive grass (Cenchrus clandestinus)-dominated ecosystems across three microtopography categories. In contrast to previous studies using the same HSI, we found that microtopography was a poor predictor of pre-disturbance conditions for soil nutrients, organic matter content, or foliar C:N, within both Dodonaea and Cenchrus vegetation types. In invader-dominated Cenchrus plots, microtopography helped predict cover, but not as expected (i.e., highest cover would be in high-suitability plots): D. viscosa had the greatest cover in low-suitability and C. clandestinus had the greatest cover in medium-suitability plots. Similarly, in native-dominated Dodonaea plots, microtopography was a poor predictor of D. viscosa, C. clandestinus, and total plant cover. Although we found some evidence that microtopography helped inform post-disturbance plant recovery of D. viscosa and total plant cover, vegetation type was a more important predictor. Important for considering the success of plantings, percent cover of D. viscosa decreased while percent cover of C. clandestinus increased within both vegetation types 20 months after disturbance. Our results are evidence that HSIs based on topographic features may prove most useful for choosing planting sites in harsh habitats or those already dominated by native species. In more productive habitats, competition from resident species may offset any benefits gained from “better” suitability sites.

","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2690","usgsCitation":"Yelenik, S.G., Rose, E., Cordell, S., Victoria, M., and Kellner, J.R., 2022, The role of microtopography and resident species in post-disturbance recovery of arid habitats in Hawaiʻi: Ecological Applications, v. 32, e2690, 18 p., https://doi.org/10.1002/eap.2690.","productDescription":"e2690, 18 p.","ipdsId":"IP-080159","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":447453,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.2690","text":"Publisher Index Page"},{"id":419497,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"Army Pohakuloa Training Area, Keʻamuku maneuver area, Hawaii Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -155.75116384910783,\n 19.925062000236125\n ],\n [\n -155.7454528828605,\n 19.776779210079724\n ],\n [\n -155.6101203582919,\n 19.781480563216192\n ],\n [\n -155.65866357139447,\n 19.937813032695132\n ],\n [\n -155.75116384910783,\n 19.925062000236125\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"32","noUsgsAuthors":false,"publicationDate":"2022-08-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Yelenik, Stephanie G. 0000-0002-9011-0769 syelenik@usgs.gov","orcid":"https://orcid.org/0000-0002-9011-0769","contributorId":5251,"corporation":false,"usgs":true,"family":"Yelenik","given":"Stephanie","email":"syelenik@usgs.gov","middleInitial":"G.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":879447,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rose, Eli 0000-0003-0958-9491 etrose@usgs.gov","orcid":"https://orcid.org/0000-0003-0958-9491","contributorId":194190,"corporation":false,"usgs":true,"family":"Rose","given":"Eli","email":"etrose@usgs.gov","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":879448,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cordell, Susan","contributorId":197818,"corporation":false,"usgs":false,"family":"Cordell","given":"Susan","email":"","affiliations":[],"preferred":false,"id":879449,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Victoria, Michelle","contributorId":317848,"corporation":false,"usgs":false,"family":"Victoria","given":"Michelle","email":"","affiliations":[{"id":69168,"text":"Pacific Internship Programs for Exploring Science, University of Hawai‘i at Hilo","active":true,"usgs":false}],"preferred":false,"id":879450,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kellner, James R.","contributorId":172034,"corporation":false,"usgs":false,"family":"Kellner","given":"James","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":879451,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70232415,"text":"70232415 - 2022 - Analysis of surface water trends for the conterminous United States using MODIS satellite data, 2003–2019","interactions":[],"lastModifiedDate":"2022-07-01T12:26:23.085105","indexId":"70232415","displayToPublicDate":"2022-06-13T07:24:13","publicationYear":"2022","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":"Analysis of surface water trends for the conterminous United States using MODIS satellite data, 2003–2019","docAbstract":"

Satellite imagery is commonly used to map surface water extents over time, but many approaches yield discontinuous records resulting from cloud obstruction or image archive gaps. We applied the Dynamic Surface Water Extent (DSWE) model to downscaled (250-m) daily Moderate Resolution Imaging Spectroradiometer (MODIS) data in Google Earth Engine to generate monthly surface water maps for the conterminous United States (US) from 2003 through 2019. The aggregation of daily observations to monthly maps of maximum water extent produced records with diminished cloud and cloud shadow effects across most of the country. We used the continuous monthly record to analyze spatiotemporal surface water trends stratified within Environmental Protection Agency Ecoregions. Although not all ecoregion trends were significant (p < 0.05), results indicate that much of the western and eastern US underwent a decline in surface water over the 17-year period, while many ecoregions in the Great Plains had positive trends. Trends were also generated from monthly streamgage discharge records and compared to surface water trends from the same ecoregion. These approaches agreed on the directionality of trend detected for 54 of 85 ecoregions, particularly across the Great Plains and portions of the western US, whereas trends were not congruent in select western deserts, the Great Lakes region, and the southeastern US. By describing the geographic distribution of surface water over time and comparing these records to instrumented discharge data across the conterminous US, our findings demonstrate the efficacy of using satellite imagery to monitor surface water dynamics and supplement traditional instrumented monitoring.

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rpetrakis@usgs.gov","orcid":"https://orcid.org/0000-0001-8932-077X","contributorId":174623,"corporation":false,"usgs":true,"family":"Petrakis","given":"Roy","email":"rpetrakis@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":845474,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Soulard, Christopher E. 0000-0002-5777-9516 csoulard@usgs.gov","orcid":"https://orcid.org/0000-0002-5777-9516","contributorId":2642,"corporation":false,"usgs":true,"family":"Soulard","given":"Christopher","email":"csoulard@usgs.gov","middleInitial":"E.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":845475,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Waller, Eric K. 0000-0002-9169-9210","orcid":"https://orcid.org/0000-0002-9169-9210","contributorId":203496,"corporation":false,"usgs":true,"family":"Waller","given":"Eric","email":"","middleInitial":"K.","affiliations":[{"id":433,"text":"National Phenology Network","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":845476,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Walker, Jessica J. 0000-0002-3225-0317","orcid":"https://orcid.org/0000-0002-3225-0317","contributorId":207373,"corporation":false,"usgs":true,"family":"Walker","given":"Jessica J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":845477,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70232544,"text":"70232544 - 2022 - A water quality barometer for Chesapeake Bay: Assessing spatial and temporal patterns using long-term monitoring data","interactions":[],"lastModifiedDate":"2022-07-07T11:56:20.480306","indexId":"70232544","displayToPublicDate":"2022-06-13T06:51:51","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1456,"text":"Ecological Indicators","active":true,"publicationSubtype":{"id":10}},"title":"A water quality barometer for Chesapeake Bay: Assessing spatial and temporal patterns using long-term monitoring data","docAbstract":"

This paper develops a barometer that indexes water quality in the Chesapeake Bay and summarizes quality over spatial regions and temporal periods. The barometer has a basis in risk assessment and hydrology, and is a function of three different metrics of water quality relative to numerical criteria: relative frequency of criterion attainment; magnitude of deviation from a numerical criterion; and duration of criterion attainment. Metrics associated with these features are calculated at the station level, allowing flexibility for simultaneously evaluating multiple stressors, different designated uses, and physical characteristics of the water. The barometer score is then created as a geometric mean of the three metrics. The water quality barometer (WQB) station scores may be spatially aggregated to report habitat scores across a spectrum of spatial resolutions (e.g., management segment, tidal subsystem, or the whole tidal bay). Dissolved oxygen measurements in the Chesapeake Bay collected during summer seasons of 1985 to 2020 are used to evaluate water quality. The WQB score and its bootstrapped confidence interval are reported at the station, segment, tidal subsystem and whole tidal bay levels. Notably, water quality interpreted through application of the WQB with dissolved oxygen concentration data and averaged over the 29-year period of record is good (i.e. protects aquatic living resources) in tributaries such as the James River, Rappahannock River and others; but is not as good in areas such as the Upper Tributaries and the York River. Recent summaries indicate that while the water quality is improving in much of the bay and its tidal tributaries, however, there is an indication of decline in quality in the period 20182020, especially in the upper regions of the Bay. The barometer is designed around using the time series data produced by the Chesapeake Bay Programs annual monitoring strategy; the approach has application to other large water bodies with large scale monitoring programs with extended time series or for integrating information from environmental sensor systems.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2022.109022","usgsCitation":"Zahran, A., Zhang, Q., Tango, P.J., and Smith, E., 2022, A water quality barometer for Chesapeake Bay: Assessing spatial and temporal patterns using long-term monitoring data: Ecological Indicators, v. 140, 109022, 17 p., https://doi.org/10.1016/j.ecolind.2022.109022.","productDescription":"109022, 17 p.","ipdsId":"IP-137760","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":447460,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2022.109022","text":"Publisher Index Page"},{"id":403128,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, Virginia","otherGeospatial":"Chesapeake Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -77.40966796875,\n 36.56260003738545\n ],\n [\n -75.30029296875,\n 36.56260003738545\n ],\n [\n -75.30029296875,\n 39.791654835253425\n ],\n [\n -77.40966796875,\n 39.791654835253425\n ],\n [\n -77.40966796875,\n 36.56260003738545\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"140","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Zahran, A.R.","contributorId":292843,"corporation":false,"usgs":false,"family":"Zahran","given":"A.R.","email":"","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":845920,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhang, Qian 0000-0003-0500-5655","orcid":"https://orcid.org/0000-0003-0500-5655","contributorId":174393,"corporation":false,"usgs":false,"family":"Zhang","given":"Qian","email":"","affiliations":[{"id":38802,"text":"University of Maryland Center for Environmental Studies","active":true,"usgs":false}],"preferred":false,"id":845921,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Tango, Peter J. 0000-0001-6669-6969","orcid":"https://orcid.org/0000-0001-6669-6969","contributorId":292845,"corporation":false,"usgs":true,"family":"Tango","given":"Peter","email":"","middleInitial":"J.","affiliations":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"preferred":true,"id":845922,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, E.P.","contributorId":292849,"corporation":false,"usgs":false,"family":"Smith","given":"E.P.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":845923,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70234335,"text":"70234335 - 2022 - Response of riparian vegetation to short- and long-term hydrologic variation","interactions":[],"lastModifiedDate":"2022-12-01T16:01:50.778596","indexId":"70234335","displayToPublicDate":"2022-06-13T06:41:49","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1450,"text":"Ecological Applications","active":true,"publicationSubtype":{"id":10}},"title":"Response of riparian vegetation to short- and long-term hydrologic variation","docAbstract":"

Increasing demand for river water now conflicts with an increasing desire to maintain riparian ecosystems. Efficiently managing river flows for riparian vegetation requires an understanding of the time scale of flow effects, but this information is limited by the absence of long-term studies of vegetation change in response to flow variation. To investigate the influence of short- and long-term flow variability and dam operation on riparian vegetation, we determined the occurrence of 107 plant species in 133 permanent plots of known inundating discharge along the Gunnison River in Colorado on five different occasions between 1990 and 2013. Individual species moved up and down the gradient of inundating discharge coincident with increases and decreases in mean annual flow, and the correlations between flow and species occurrence were strongest when flows were weighted by time before vegetation sampling with a median half-life of 1.5 years. Some tall, rhizomatous, perennial species, however, responded to flows on a longer time scale. Logistic regression of species occurrence showed a significant relation with inundation duration for 70 out of 107 species. Plot species richness and total vegetative cover decreased in association with desiccation at low inundation durations and with fluvial disturbance at high inundation durations. Within-plot similarity in species occurrence between years decreased strongly with increasing inundation duration. Moderate inundation durations were dominated by tall, rhizomatous, perennial herbs, including invasive Phalaris arundinacea (reed canary grass). Over the 23-year study period, species richness declined, and the proportion of rhizomatous perennials increased, consistent with the hypothesis that decreases in flow peaks and increases in low flows caused by flow regulation have decreased establishment opportunities for disturbance-dependent species. In summary, annual-scale changes in vegetation were strongly influenced by flow variation, and decadal-scale changes were influenced by decreases in fluvial disturbance from upstream flow regulation beginning decades prior to the onset of this study.

","language":"English","publisher":"Wiley","doi":"10.1002/eap.2689","usgsCitation":"Friedman, J.M., Eurich, A.M., Auble, G.T., Scott, M., Shafroth, P., and Gibson, P.P., 2022, Response of riparian vegetation to short- and long-term hydrologic variation: Ecological Applications, v. 32, no. 8, e2689, 16 p., https://doi.org/10.1002/eap.2689.","productDescription":"e2689, 16 p.","ipdsId":"IP-128733","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":447464,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/eap.2689","text":"Publisher Index Page"},{"id":435807,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91BEXPC","text":"USGS data release","linkHelpText":"Occurrence of plants in plots along the Gunnison River, Colorado, 1990-2017"},{"id":404988,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Gunnison River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.677001953125,\n 38.363195134453846\n ],\n [\n -106.8804931640625,\n 38.363195134453846\n ],\n [\n -106.8804931640625,\n 38.56964280859044\n ],\n [\n -107.677001953125,\n 38.56964280859044\n ],\n [\n -107.677001953125,\n 38.363195134453846\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"32","issue":"8","noUsgsAuthors":false,"publicationDate":"2022-08-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Friedman, Jonathan M. 0000-0002-1329-0663","orcid":"https://orcid.org/0000-0002-1329-0663","contributorId":44495,"corporation":false,"usgs":true,"family":"Friedman","given":"Jonathan","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":848585,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Eurich, Abigail M. 0000-0001-9891-3876","orcid":"https://orcid.org/0000-0001-9891-3876","contributorId":294681,"corporation":false,"usgs":false,"family":"Eurich","given":"Abigail","email":"","middleInitial":"M.","affiliations":[{"id":63625,"text":"Under Contract to USGS Fort Collins Science Center","active":true,"usgs":false}],"preferred":false,"id":848586,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Auble, Gregor T. 0000-0002-0843-2751","orcid":"https://orcid.org/0000-0002-0843-2751","contributorId":294682,"corporation":false,"usgs":false,"family":"Auble","given":"Gregor","email":"","middleInitial":"T.","affiliations":[{"id":37421,"text":"Retired U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":848587,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Scott, Michael L.","contributorId":244803,"corporation":false,"usgs":false,"family":"Scott","given":"Michael L.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":848588,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shafroth, Patrick B. 0000-0002-6064-871X","orcid":"https://orcid.org/0000-0002-6064-871X","contributorId":225182,"corporation":false,"usgs":true,"family":"Shafroth","given":"Patrick B.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":848589,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gibson, Polly P 0000-0002-9751-7895","orcid":"https://orcid.org/0000-0002-9751-7895","contributorId":294683,"corporation":false,"usgs":false,"family":"Gibson","given":"Polly","email":"","middleInitial":"P","affiliations":[{"id":63625,"text":"Under Contract to USGS Fort Collins Science Center","active":true,"usgs":false}],"preferred":false,"id":848590,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70262376,"text":"70262376 - 2022 - The need to step-up monitoring of Asian bears","interactions":[],"lastModifiedDate":"2025-01-24T14:24:19.146882","indexId":"70262376","displayToPublicDate":"2022-06-12T10:13:57","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"The need to step-up monitoring of Asian bears","docAbstract":"

Many wildlife species are threatened in Asia, including the five species of terrestrial bears (Asiatic black, Ursus thibetanus; brown, U. arctos; sloth, Melursus ursinus; sun, Helarctos malayanus; giant panda, Ailuropoda melanoleuca): many populations of these bears are thought to be declining or imperiled by small population size. Here our aim is to document how population assessments have been conducted for bears in Asia. We searched the literature and identified 102 studies published during 1999–2021 that investigated the status of an Asian bear population; these occurred in 24 of the 32 bear range countries in Asia. At the most basic level, 11% of studies verified presence of bears in places where they were not known to exist. The most common objective (53% of papers) was a distribution map, often derived from presence locations in a habitat-based model. Occupancy studies (15%) used temporal (time stamps on images from cameras) or spatial (transect segments) replicates, but tended to focus on “use”, so detector spacing was sometimes not appropriate for occupancy. Purported population indices, such as sign density or camera trap encounter rates, were reported in 16% of studies. One third of studies provided a population estimate, but only 10 studies in two decades used a rigorous method (e.g., mark–recapture). Sign surveys and interviews were the most common methods for determining bear presence, and local interviews were heavily relied upon for assessing population trend. Camera trapping has become increasingly prevalent, but only one study obtained a population estimate using photographs to distinguish natural individual markings. Only three studies used hair traps to obtain DNA-based population estimates, and three other studies obtained population estimates from DNA in scats. Just three studies quantitatively measured change in population size or occupancy over time, and none of these showed a decline. Unique rangewide sign surveys of giant pandas showed significant geographic expansion. The opinions of experts and local people, now heavily relied upon for population assessments, are not reliable or sensitive enough for monitoring. Quantitative population assessments are desirable to direct conservation actions toward the most perilous situations, and provide a means to gauge the effectiveness of conservation actions. This paper demonstrates the paucity of rigorous monitoring of Asian bears, and leads off a series of papers that propose improved methods for assessing distribution, occupancy, and density

","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2022.e02087","usgsCitation":"Garshelis, D., Pigeon, K., Hwang, M., Proctor, M., McShea, W., Fuller, A.K., and Morin, D., 2022, The need to step-up monitoring of Asian bears: Global Ecology and Conservation, v. 35, e02087, 13 p., https://doi.org/10.1016/j.gecco.2022.e02087.","productDescription":"e02087, 13 p.","ipdsId":"IP-135711","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481082,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2022.e02087","text":"Publisher Index Page"},{"id":481003,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Asia","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 35.41529707232115,\n 46.71177016110596\n ],\n [\n 38.775261932193075,\n 32.68713053487669\n ],\n [\n 52.43784583457693,\n 25.45805552202023\n ],\n [\n 66.05615094699141,\n 24.425093270056593\n ],\n [\n 73.35126800461401,\n 10.197546076498313\n ],\n [\n 81.23886207886909,\n 4.4314691000760575\n ],\n [\n 91.04270936032952,\n 20.575934862650513\n ],\n [\n 94.1901763972345,\n 15.202352382026703\n ],\n [\n 97.1384405012642,\n 15.069087431430717\n ],\n [\n 94.6771672603644,\n 5.283116189461083\n ],\n [\n 102.35786027086067,\n -7.160654452094249\n ],\n [\n 114.53893138055798,\n -9.948047350337688\n ],\n [\n 126.0483612530345,\n 1.5862440839753162\n ],\n [\n 110.48966479958608,\n 11.165967994207222\n ],\n [\n 109.5109961667568,\n 15.596355224751335\n ],\n [\n 123.11341991330539,\n 27.830415332171597\n ],\n [\n 117.41469263762411,\n 41.60352364743272\n ],\n [\n 126.3476015754286,\n 34.01603489861127\n ],\n [\n 130.8382936110803,\n 30.435437531184533\n ],\n [\n 142.09430597156933,\n 34.06146267056606\n ],\n [\n 145.66944166800903,\n 45.9562112664336\n ],\n [\n 140.1217329834799,\n 55.995966254587444\n ],\n [\n 151.80803406522398,\n 57.96021145528408\n ],\n [\n 157.6450054371361,\n 50.79973619380252\n ],\n [\n 194.26952722093165,\n 65.86713544718819\n ],\n [\n 109.61559776142303,\n 79.02811684931939\n ],\n [\n 44.20411956221898,\n 66.72535345333856\n ],\n [\n 21.749680729417662,\n 55.76507789697047\n ],\n [\n 35.41529707232115,\n 46.71177016110596\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"35","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Garshelis, David L.","contributorId":349064,"corporation":false,"usgs":false,"family":"Garshelis","given":"David L.","affiliations":[{"id":83340,"text":"IUCN SSC Bear Specialist Group","active":true,"usgs":false}],"preferred":false,"id":923977,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pigeon, Karine","contributorId":349066,"corporation":false,"usgs":false,"family":"Pigeon","given":"Karine","affiliations":[{"id":83421,"text":"BC Ministry of Forests","active":true,"usgs":false}],"preferred":false,"id":923978,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hwang, Mei-hsiu","contributorId":349068,"corporation":false,"usgs":false,"family":"Hwang","given":"Mei-hsiu","affiliations":[{"id":83423,"text":"National Pingtung University of Science and Technology","active":true,"usgs":false}],"preferred":false,"id":923979,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Proctor, Michael","contributorId":349069,"corporation":false,"usgs":false,"family":"Proctor","given":"Michael","affiliations":[{"id":83340,"text":"IUCN SSC Bear Specialist Group","active":true,"usgs":false}],"preferred":false,"id":923980,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McShea, William J.","contributorId":349070,"corporation":false,"usgs":false,"family":"McShea","given":"William J.","affiliations":[{"id":37784,"text":"Smithsonian Conservation Biology Institute","active":true,"usgs":false}],"preferred":false,"id":923981,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fuller, Angela K. 0000-0002-9247-7468 afuller@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-7468","contributorId":3984,"corporation":false,"usgs":true,"family":"Fuller","given":"Angela","email":"afuller@usgs.gov","middleInitial":"K.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":923976,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Morin, Dana J.","contributorId":349071,"corporation":false,"usgs":false,"family":"Morin","given":"Dana J.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":923982,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70232212,"text":"70232212 - 2022 - Predicting near-term effects of climate change on nitrogen transport to Chesapeake Bay","interactions":[],"lastModifiedDate":"2022-08-15T13:54:09.728265","indexId":"70232212","displayToPublicDate":"2022-06-12T09:12:01","publicationYear":"2022","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":"Predicting near-term effects of climate change on nitrogen transport to Chesapeake Bay","docAbstract":"

Understanding effects of climate change on nitrogen fate and transport in the environment is critical to nutrient management. We used climate projections within a previously calibrated spatially referenced regression (SPARROW) model to predict effects of expected climate change over 1995 through 2025 on total nitrogen fluxes to Chesapeake Bay and in watershed streams. Assuming nitrogen inputs and other watershed conditions remain at 2012 levels, effects of increasing temperature, runoff, streamflow, and stream velocity expected between 1995 and 2025 will include an estimated net 6.5% decline in annual nitrogen delivery to the bay from its watershed. This predicted decline is attributable to declines in the delivery of nitrogen from upland nonpoint sources to streams due to predicted warmer temperatures. Such temperature-driven declines in the delivery of nitrogen to streams more than offset predicted increased delivery to and within streams due to increased runoff and streamflow and may be attributable to increasing rates of denitrification or ammonia volatilization or to changes in plant phenology. Predicted climate-driven declines in nitrogen flux are generally similar across the watershed but vary slightly among major nonpoint source sectors and tributary watersheds. Nitrogen contributions to the bay from point sources are not affected by temperature-driven changes in delivery from uplands and are therefore predicted to increase slightly between 1995 and 2025.

","language":"English","publisher":"American Water Resources Association","doi":"10.1111/1752-1688.13017","usgsCitation":"Ator, S., Schwarz, G.E., Sekellick, A.J., and Bhatt, G., 2022, Predicting near-term effects of climate change on nitrogen transport to Chesapeake Bay: Journal of the American Water Resources Association, v. 58, no. 4, p. 578-596, https://doi.org/10.1111/1752-1688.13017.","productDescription":"19 p.","startPage":"578","endPage":"596","ipdsId":"IP-125426","costCenters":[{"id":41514,"text":"Maryland-Delaware-District of Columbia Water Science Center","active":true,"usgs":true}],"links":[{"id":447466,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1752-1688.13017","text":"Publisher Index Page"},{"id":435808,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IYEOKW","text":"USGS data release","linkHelpText":"SPARROW model input datasets and predictions for predicting near-term effects of 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A geoenvironmental assessment methodology was developed to estimate waste quantities and disturbances that could be associated with the extraction of undiscovered uranium resources and identify areas on the landscape where uranium and other constituents of potential concern (COPCs) that may co-occur with uranium deposits in this region are likely to persist, if introduced into the environment. Prior to this work, a method was lacking to quantitively assess the environmental aspects associated with potential development of undiscovered uranium resources at a scale of a uranium resource assessment. The mining method of in situ recovery (ISR) was historically used to extract uranium from deposits in the Goliad Sand of the Texas Coastal Plain. For this reason, the study’s methodology projected the following types of wastes and disturbances commonly associated with ISR based on historical ISR mining records: the mine area, affected aquifer volume, mine pore volume, water pumped and disposed during uranium extraction and restoration, and radon emissions. Within the tract permissive for the occurrence of undiscovered uranium resources, maps and statistics of factors were derived that indicate the potential contaminant pathways. The percentage of days meeting the criteria for air stagnation indicate the potential for radon accumulation; the geochemical mobility of COPCs in groundwater in combination with effective recharge indicates the potential for infiltration of surface-derived COPCs; the geochemical mobility of COPCs in groundwater combined with hydraulic conductivity indicates the propensity for transmitting fluids away from contaminated or mined aquifers; and finally, geochemical mobility of COPCs in surface water combined with the factor for climatic erosivity (R factor) indicates the potential for COPCs to persist in surface waters due to runoff. This work resulted in a new methodology that can be applied to any undiscovered mineral resource to better understand possible wastes and disturbances associated with extraction and identify areas on the landscape where COPCs are likely to persist.
","language":"English","publisher":"MDPI","doi":"10.3390/min12060747","usgsCitation":"Gallegos, T., Stengel, V.G., Walton-Day, K., Blake, J., Teeple, A., Humberson, D.G., Cahan, S., Yager, D., and Becher, K.D., 2022, A novel method for conducting a geoenvironmental assessment of undiscovered ISR-amenable uranium Resources: Proof-of-concept in the Texas Coastal Plain: Minerals, v. 12, no. 6, 747, 21 p., https://doi.org/10.3390/min12060747.","productDescription":"747, 21 p.","ipdsId":"IP-136342","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true},{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":447469,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/min12060747","text":"Publisher Index Page"},{"id":418741,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96.9175352631714,\n 25.819063287006443\n ],\n [\n -96.82968233171721,\n 27.662685556706492\n ],\n [\n -95.42403542845311,\n 28.438008900802856\n ],\n [\n -93.49127093646484,\n 29.322651774069968\n ],\n [\n -93.49127093646484,\n 30.31351672242603\n ],\n [\n -93.49127093646484,\n 30.993701428168237\n ],\n [\n -93.97446205946191,\n 31.96767219212434\n ],\n [\n -94.06231499091611,\n 32.931418983082224\n ],\n [\n -94.54550611391319,\n 33.33604026376062\n ],\n [\n -96.47827060590144,\n 31.85580951863453\n ],\n [\n -98.5867409607976,\n 29.781194146196455\n ],\n [\n -99.94846139833513,\n 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vstengel@usgs.gov","orcid":"https://orcid.org/0000-0003-0481-3159","contributorId":5932,"corporation":false,"usgs":true,"family":"Stengel","given":"Victoria","email":"vstengel@usgs.gov","middleInitial":"G.","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":877022,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Walton-Day, Katherine 0000-0002-9146-6193 kwaltond@usgs.gov","orcid":"https://orcid.org/0000-0002-9146-6193","contributorId":184043,"corporation":false,"usgs":true,"family":"Walton-Day","given":"Katherine","email":"kwaltond@usgs.gov","affiliations":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"preferred":true,"id":877023,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Blake, Johanna 0000-0003-4667-0096","orcid":"https://orcid.org/0000-0003-4667-0096","contributorId":217272,"corporation":false,"usgs":true,"family":"Blake","given":"Johanna","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":877024,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Teeple, Andrew 0000-0003-1781-8354 apteeple@usgs.gov","orcid":"https://orcid.org/0000-0003-1781-8354","contributorId":193061,"corporation":false,"usgs":true,"family":"Teeple","given":"Andrew","email":"apteeple@usgs.gov","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":877025,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Humberson, Delbert G 0000-0001-6789-9135","orcid":"https://orcid.org/0000-0001-6789-9135","contributorId":240891,"corporation":false,"usgs":false,"family":"Humberson","given":"Delbert","email":"","middleInitial":"G","affiliations":[],"preferred":false,"id":877026,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cahan, Steven M. 0000-0002-4776-3668","orcid":"https://orcid.org/0000-0002-4776-3668","contributorId":205929,"corporation":false,"usgs":true,"family":"Cahan","given":"Steven M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":877027,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Yager, Douglas 0000-0001-5074-4022","orcid":"https://orcid.org/0000-0001-5074-4022","contributorId":202073,"corporation":false,"usgs":true,"family":"Yager","given":"Douglas","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science 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mechanisms of coastal woody-plant mortality","docAbstract":"

Observations of woody plant mortality in coastal ecosystems are globally widespread, but the overarching processes and underlying mechanisms are poorly understood. This knowledge deficiency, combined with rapidly changing water levels, storm surges, atmospheric CO2, and vapor pressure deficit, creates large predictive uncertainty regarding how coastal ecosystems will respond to global change. Here we synthesize the literature on the mechanisms that underlie coastal woody-plant mortality, with the goal of producing a testable hypothesis framework. The key emergent mechanisms underlying mortality include hypoxic, osmotic, and ionic-driven reductions in whole-plant hydraulic conductance and photosynthesis that ultimately drive the coupled processes of hydraulic failure and carbon starvation. The relative importance of these processes in driving mortality, their order of progression, and their degree of coupling depends on the characteristics of the anomalous water exposure, on topographic effects, and on taxa-specific variation in traits and trait acclimation. Greater inundation exposure could accelerate mortality globally; however, the interaction of changing inundation exposure with elevated CO2, drought, and rising vapor pressure deficit could influence mortality likelihood. Models of coastal forests that incorporate the frequency and duration of inundation, the role of climatic drivers, and the processes of hydraulic failure and carbon starvation can yield improved estimates of inundation-induced woody-plant mortality.

","language":"English","publisher":"Wiley","doi":"10.1111/gcb.16297","usgsCitation":"McDowell, N.G., Ball, M., Bond-Lamberty, B., Kirwan, M.L., Krauss, K., Megonigal, J.P., Mencuccini, M., Ward, N.D., Weintraub, M., and Bailey, V., 2022, Processes and mechanisms of coastal woody-plant mortality: Global Change Biology, v. 28, no. 20, p. 5881-5900, https://doi.org/10.1111/gcb.16297.","productDescription":"20 p.","startPage":"5881","endPage":"5900","ipdsId":"IP-138484","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":447473,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1111/gcb.16297","text":"External Repository"},{"id":404111,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"28","issue":"20","noUsgsAuthors":false,"publicationDate":"2022-07-29","publicationStatus":"PW","contributors":{"authors":[{"text":"McDowell, Nate G.","contributorId":207743,"corporation":false,"usgs":false,"family":"McDowell","given":"Nate","email":"","middleInitial":"G.","affiliations":[{"id":37622,"text":"Earth Systems Science Division, Pacific Northwest National Laboratory","active":true,"usgs":false}],"preferred":false,"id":847019,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ball, Marilyn","contributorId":293463,"corporation":false,"usgs":false,"family":"Ball","given":"Marilyn","affiliations":[{"id":16807,"text":"Australian National University","active":true,"usgs":false}],"preferred":false,"id":847020,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bond-Lamberty, Ben","contributorId":224752,"corporation":false,"usgs":false,"family":"Bond-Lamberty","given":"Ben","email":"","affiliations":[{"id":40935,"text":"Joint Global Research Institute, Maryland","active":true,"usgs":false}],"preferred":false,"id":847021,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kirwan, Matthew L.","contributorId":191373,"corporation":false,"usgs":false,"family":"Kirwan","given":"Matthew","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":847022,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Krauss, Ken 0000-0003-2195-0729","orcid":"https://orcid.org/0000-0003-2195-0729","contributorId":221936,"corporation":false,"usgs":true,"family":"Krauss","given":"Ken","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":847023,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Megonigal, J. Patrick","contributorId":288317,"corporation":false,"usgs":false,"family":"Megonigal","given":"J.","email":"","middleInitial":"Patrick","affiliations":[{"id":13510,"text":"Smithsonian Environmental Research Center","active":true,"usgs":false}],"preferred":false,"id":847024,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mencuccini, Maurizio","contributorId":199454,"corporation":false,"usgs":false,"family":"Mencuccini","given":"Maurizio","email":"","affiliations":[],"preferred":false,"id":847025,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ward, Nicholas D.","contributorId":293465,"corporation":false,"usgs":false,"family":"Ward","given":"Nicholas","email":"","middleInitial":"D.","affiliations":[{"id":40277,"text":"U.S. Department of Energy","active":true,"usgs":false}],"preferred":false,"id":847026,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Weintraub, Michael N.","contributorId":293467,"corporation":false,"usgs":false,"family":"Weintraub","given":"Michael N.","affiliations":[{"id":12455,"text":"University of Toledo","active":true,"usgs":false}],"preferred":false,"id":847027,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Bailey, Vanessa","contributorId":224753,"corporation":false,"usgs":false,"family":"Bailey","given":"Vanessa","email":"","affiliations":[{"id":38914,"text":"Pacific Northwest National Laboratory","active":true,"usgs":false}],"preferred":false,"id":847028,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70263175,"text":"70263175 - 2022 - Habitat edges influence the distribution of nest predators for Seaside Sparrows, but not nest placement or success","interactions":[],"lastModifiedDate":"2025-01-31T15:03:44.825151","indexId":"70263175","displayToPublicDate":"2022-06-10T08:54:49","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9101,"text":"Ornithological Applications","printIssn":"0010-5422","active":true,"publicationSubtype":{"id":10}},"title":"Habitat edges influence the distribution of nest predators for Seaside Sparrows, but not nest placement or success","docAbstract":"

Nest failure for coastal marsh bird species is primarily caused by predation and nest flooding. As sea level rise makes nest flooding more likely, the threat of nest predation will constrain the potential adaptive responses of marsh nesting species. Thus, understanding the predictors of nest predation is important for the conservation of salt marsh-dwelling bird species, such as the Seaside Sparrow (Ammospiza maritima). Predator activity may be influenced by landscape features (particularly habitat edges), potentially making nest predation predictable. We aimed to understand the predictability of Seaside Sparrow nest predation relative to two major landscape features: roads and tidal rivers, as both of these edges may be entryways or attractants for predators in marshes. In coastal Georgia, USA, we assessed mammalian predator activity relative to the two features of interest, and hypothesized that mammalian predator activity would be greater close to roads and tidal rivers. We also recorded Seaside Sparrow nest locations and nest predation events and hypothesized that nest predation events would increase with increasing predator activity. Consistent with our first hypothesis, mammalian predator activity increased close to roads and tidal rivers, but mammalian predator distribution did not explain the spatial variation in Seaside Sparrow nest predation thus not supporting our second hypothesis. Seaside Sparrows also placed their nests in locations with high mammalian predator activity, indicating that the ability to avoid nesting in high-risk areas may be constrained by habitat or resource limitations. Additionally, mammals may not be the primary nest predators, as we found that one bird species—Marsh Wren (Cistothorus palustris)—contributed substantially to nest predation rates. Understanding the predictability of mammalian predator distribution can allow for focused predator management efforts, such as exclusion, to habitat edges where we found the highest mammalian predator activity, which could relax the constraint of nest predation on Seaside Sparrow’s ability to respond to the intensifying threat of sea level rise.

","language":"English","publisher":"Oxford Academic","doi":"10.1093/ornithapp/duac023","usgsCitation":"Newsome, C., and Hunter, E.A., 2022, Habitat edges influence the distribution of nest predators for Seaside Sparrows, but not nest placement or success: Ornithological Applications, v. 124, no. 3, duac023, 13 p., https://doi.org/10.1093/ornithapp/duac023.","productDescription":"duac023, 13 p.","ipdsId":"IP-132343","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":489864,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/ornithapp/duac023","text":"Publisher Index Page"},{"id":481544,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Georgia","county":"Glynn 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Corina D.","contributorId":350338,"corporation":false,"usgs":false,"family":"Newsome","given":"Corina D.","affiliations":[{"id":16976,"text":"Georgia Southern University","active":true,"usgs":false}],"preferred":false,"id":925783,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hunter, Elizabeth Ann 0000-0003-4710-167X","orcid":"https://orcid.org/0000-0003-4710-167X","contributorId":288535,"corporation":false,"usgs":true,"family":"Hunter","given":"Elizabeth","email":"","middleInitial":"Ann","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":925782,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70246521,"text":"70246521 - 2022 - U-Pb and fission-track data from zircon and apatite resolve latest- and post-Alleghanian thermal histories along the Fall Line of the Atlantic margin of the southeastern United States","interactions":[],"lastModifiedDate":"2023-07-07T12:21:17.568399","indexId":"70246521","displayToPublicDate":"2022-06-10T07:18:14","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"U-Pb and fission-track data from zircon and apatite resolve latest- and post-Alleghanian thermal histories along the Fall Line of the Atlantic margin of the southeastern United States","docAbstract":"

Although the Atlantic continental margin of the eastern United States is an archetypal passive margin, episodes of rejuvenation following continental breakup are increasingly well documented. To better constrain this history of rejuvenation along the southern portion of this continental margin, we present zircon U-Pb (ZUPb) age, zircon fission-track (ZFT) age, apatite U-Pb (AUPb) age, and apatite fission-track (AFT) age and length data from six bedrock samples. The samples were collected along the boundary between the exposed Appalachian hinterland (Piedmont province) and the updip limit of passive margin strata (Coastal Plain province). The samples were collected from central Virginia southward to the South Carolina–Georgia border. ZUPb age distributions are generally consistent with geologic mapping in each of the sample areas. The AUPb data are highly discordant owing to high common-Pb abundances, but for two plutons at the northern and southern ends of the sample area, they define a discordia regression line that indicates substantial Permo-Triassic exhumation-driven cooling. ZFT age distributions are highly dispersed but define central values ranging from Permian to Jurassic. AFT data mostly appear to define a singular underlying cooling age, generally approximately Jurassic or Early Cretaceous. Apatite fission tracks are moderately long (mean lengths in the range of ~13.5 µm), however track lengths for one sample in central North Carolina are shorter (~12.5 µm). To interpret the post-breakup thermal history, we present inverse models of time-temperature history for the five plutonic samples. The models show a history of (1) rapid cooling (>10 °C/m.y.) from deep-crustal to near-surface temperatures by the Triassic, (2) hundreds of degrees of Triassic reheating, (3) Jurassic–Early Cretaceous cooling (at rates of 1–10 °C/m.y.), and (4) slow Late Cretaceous–Cenozoic cooling (~1 °C/m.y.). An additional suite of forward models is presented to further evaluate the magnitude of maximum Triassic reheating at one sample site that is particularly well constrained by thermal maturity data. The model results and geologic reasoning suggest that the inverse models may overestimate Triassic paleotemperatures but that other aspects of the inverse modeling are robust. Overall, this thermal history can be reconciled with several aspects of the lithostratigraphy of distal parts of the continental margin, including the lack of Jurassic–earliest Cretaceous strata beneath the southern Atlantic coastal plain and Cretaceous–Cenozoic grain-size trends.

","language":"English","publisher":"Geological Society of America","doi":"10.1130/GES02447.1","usgsCitation":"Craddock, W.H., O'Sullivan, P., and McAleer, R.J., 2022, U-Pb and fission-track data from zircon and apatite resolve latest- and post-Alleghanian thermal histories along the Fall Line of the Atlantic margin of the southeastern United States: Geosphere, v. 18, no. 4, p. 1330-1353, https://doi.org/10.1130/GES02447.1.","productDescription":"24 p.","startPage":"1330","endPage":"1353","ipdsId":"IP-127916","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":447476,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1130/ges02447.1","text":"Publisher Index Page"},{"id":418744,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.06589631767767,\n 40.62761013989578\n ],\n [\n -76.81264623503682,\n 40.32689154960596\n ],\n [\n -79.40430771293059,\n 40.02563596082908\n ],\n [\n -81.51277806782672,\n 38.76941324786962\n ],\n [\n -83.62124842272343,\n 36.33152035458974\n ],\n [\n -84.63155713444466,\n 33.778910284254295\n ],\n [\n -83.84088075135841,\n 32.7874625650833\n ],\n [\n -82.1277485880051,\n 31.4481799351054\n ],\n [\n -80.32676349319762,\n 31.672755039272587\n ],\n [\n -77.51546968666938,\n 33.22949605942023\n ],\n [\n -75.14344053741085,\n 35.02409793894682\n ],\n [\n -73.12282311396812,\n 37.10613979176382\n ],\n [\n -73.47423483978443,\n 39.24678773255735\n ],\n [\n -76.06589631767767,\n 40.62761013989578\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"18","issue":"4","noUsgsAuthors":false,"publicationDate":"2022-06-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Craddock, William H. 0000-0002-4181-4735 wcraddock@usgs.gov","orcid":"https://orcid.org/0000-0002-4181-4735","contributorId":3411,"corporation":false,"usgs":true,"family":"Craddock","given":"William","email":"wcraddock@usgs.gov","middleInitial":"H.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":877037,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O'Sullivan, Paul","contributorId":257903,"corporation":false,"usgs":false,"family":"O'Sullivan","given":"Paul","affiliations":[{"id":51089,"text":"Geosep Services","active":true,"usgs":false}],"preferred":false,"id":877038,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"McAleer, Ryan J. 0000-0003-3801-7441 rmcaleer@usgs.gov","orcid":"https://orcid.org/0000-0003-3801-7441","contributorId":215498,"corporation":false,"usgs":true,"family":"McAleer","given":"Ryan","email":"rmcaleer@usgs.gov","middleInitial":"J.","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":877039,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70232150,"text":"ofr20221033 - 2022 - ECCOE Landsat Quarterly Calibration and Validation report— Quarter 4, 2021","interactions":[],"lastModifiedDate":"2022-09-27T12:38:02.126827","indexId":"ofr20221033","displayToPublicDate":"2022-06-10T07:10:38","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1033","displayTitle":"ECCOE Landsat Quarterly Calibration and Validation Report—Quarter 4, 2021","title":"ECCOE Landsat Quarterly Calibration and Validation report— Quarter 4, 2021","docAbstract":"

Executive Summary

The U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Calibration and Validation (Cal/Val) Center of Excellence (ECCOE) focuses on improving the accuracy, precision, calibration, and product quality of remote-sensing data, leveraging years of multiscale optical system geometric and radiometric calibration and characterization experience. The ECCOE Landsat Cal/Val Team continually monitors the geometric and radiometric performance of active Landsat missions and makes calibration adjustments, as needed, to maintain data quality at the highest level.

This report provides observed geometric and radiometric analysis results for Landsats 7–8 for quarter 4 (October–December), 2021. All data used to compile the Cal/Val analysis results presented in this report are freely available from the USGS EarthExplorer website: https://earthexplorer.usgs.gov.

One specific activity that the Cal/Val Team continued to closely monitor this quarter was the Landsat 8 Thermal Infrared Sensor (TIRS) response degradation, which has been observed since the two November 2020 safehold events. Detailed analysis results characterizing this degradation have been included in this report. Additional information about the safehold events is here: https://www.usgs.gov/core-science-systems/nli/landsat/november-19-2020-landsat-8-data-availability-update-recent-safehold.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221033","usgsCitation":"Haque, M.O., Rengarajan, R., Lubke, M., Tuli, F.T.Z., Shaw, J.L., Hasan, M.N., Denevan, A., Franks, S., Micijevic, E., Choate, M.J., Anderson, C., Markham, B., Thome, K., Kaita, E., Barsi, J., Levy, R., and Ong, L., 2022, ECCOE Landsat Quarterly Calibration and Validation report— Quarter 4, 2021: U.S. Geological Survey Open-File Report 2022–1033, 38 p., https://doi.org/10.3133/ofr20221033.","productDescription":"Report: vii, 38 p.; Dataset","numberOfPages":"50","onlineOnly":"Y","ipdsId":"IP-137795","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":401936,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1033/ofr20221033.pdf","text":"Report","size":"3.39 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1033"},{"id":401934,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1033/coverthb.jpg"},{"id":402059,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221033/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":401939,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://earthexplorer.usgs.gov/","text":"USGS database","linkHelpText":"—EarthExplorer"},{"id":401938,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1033/images"},{"id":401937,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1033/ofr20221033.XML"}],"contact":"

Director, Earth Resources Observation and Science Center
U.S. Geological Survey
47914 252nd Street
Sioux Falls, SD 57198

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-06-10","noUsgsAuthors":false,"publicationDate":"2022-06-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Haque, Obaidul 0000-0002-0914-1446 ohaque@usgs.gov","orcid":"https://orcid.org/0000-0002-0914-1446","contributorId":4691,"corporation":false,"usgs":true,"family":"Haque","given":"Obaidul","email":"ohaque@usgs.gov","affiliations":[{"id":40546,"text":"KBR, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":true,"id":844344,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rengarajan, Rajagopalan 0000-0003-1860-7110 rrengarajan@contractor.usgs.gov","orcid":"https://orcid.org/0000-0003-1860-7110","contributorId":192376,"corporation":false,"usgs":true,"family":"Rengarajan","given":"Rajagopalan","email":"rrengarajan@contractor.usgs.gov","affiliations":[{"id":40546,"text":"KBR, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":true,"id":844345,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lubke, Mark 0000-0002-7257-2337","orcid":"https://orcid.org/0000-0002-7257-2337","contributorId":261911,"corporation":false,"usgs":false,"family":"Lubke","given":"Mark","email":"","affiliations":[{"id":53079,"text":"KBR, contractor to U.S. Geological Survey","active":true,"usgs":false}],"preferred":false,"id":844346,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tuz Zafrin Tuli, Fatima 0000-0002-5225-8797","orcid":"https://orcid.org/0000-0002-5225-8797","contributorId":270395,"corporation":false,"usgs":false,"family":"Tuz Zafrin Tuli","given":"Fatima","email":"","affiliations":[{"id":40546,"text":"KBR, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":844347,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shaw, Jerad L. 0000-0002-8319-2778","orcid":"https://orcid.org/0000-0002-8319-2778","contributorId":270396,"corporation":false,"usgs":false,"family":"Shaw","given":"Jerad L.","affiliations":[{"id":40546,"text":"KBR, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":844348,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hasan, Nahid 0000-0002-0463-601X","orcid":"https://orcid.org/0000-0002-0463-601X","contributorId":292342,"corporation":false,"usgs":false,"family":"Hasan","given":"Nahid","email":"","affiliations":[{"id":40546,"text":"KBR, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":844349,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Denevan, Alex 0000-0002-1215-3261","orcid":"https://orcid.org/0000-0002-1215-3261","contributorId":270398,"corporation":false,"usgs":false,"family":"Denevan","given":"Alex","email":"","affiliations":[{"id":40546,"text":"KBR, Contractor to the USGS Earth Resources Observation and Science (EROS) Center","active":true,"usgs":false}],"preferred":false,"id":844350,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Franks, Shannon 0000-0003-1335-5401","orcid":"https://orcid.org/0000-0003-1335-5401","contributorId":93362,"corporation":false,"usgs":true,"family":"Franks","given":"Shannon","affiliations":[],"preferred":false,"id":844351,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Micijevic, Esad 0000-0002-3828-9239 emicijevic@usgs.gov","orcid":"https://orcid.org/0000-0002-3828-9239","contributorId":3075,"corporation":false,"usgs":true,"family":"Micijevic","given":"Esad","email":"emicijevic@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":844352,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Choate, Mike 0000-0002-8101-4994 choate@usgs.gov","orcid":"https://orcid.org/0000-0002-8101-4994","contributorId":4618,"corporation":false,"usgs":true,"family":"Choate","given":"Mike","email":"choate@usgs.gov","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true}],"preferred":true,"id":844353,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Anderson, Cody 0000-0001-5612-1889 chanderson@usgs.gov","orcid":"https://orcid.org/0000-0001-5612-1889","contributorId":195521,"corporation":false,"usgs":true,"family":"Anderson","given":"Cody","email":"chanderson@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":844354,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Markham, Brian 0000-0002-9612-8169","orcid":"https://orcid.org/0000-0002-9612-8169","contributorId":139286,"corporation":false,"usgs":false,"family":"Markham","given":"Brian","affiliations":[{"id":12721,"text":"NASA GSFC SSAI","active":true,"usgs":false}],"preferred":false,"id":844355,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Thome, Kurt","contributorId":140792,"corporation":false,"usgs":false,"family":"Thome","given":"Kurt","email":"","affiliations":[{"id":7049,"text":"NASA Goddard Space Flight Center","active":true,"usgs":false}],"preferred":false,"id":844356,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Kaita, Ed","contributorId":251782,"corporation":false,"usgs":false,"family":"Kaita","given":"Ed","email":"","affiliations":[{"id":50397,"text":"SSAI","active":true,"usgs":false}],"preferred":false,"id":844357,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Barsi, Julia","contributorId":251781,"corporation":false,"usgs":false,"family":"Barsi","given":"Julia","email":"","affiliations":[{"id":50397,"text":"SSAI","active":true,"usgs":false}],"preferred":false,"id":844358,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Levy, Raviv","contributorId":131008,"corporation":false,"usgs":false,"family":"Levy","given":"Raviv","email":"","affiliations":[{"id":7209,"text":"SSAI / NASA / GSFC","active":true,"usgs":false}],"preferred":false,"id":844359,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Ong, Lawrence","contributorId":139287,"corporation":false,"usgs":false,"family":"Ong","given":"Lawrence","email":"","affiliations":[{"id":12721,"text":"NASA GSFC SSAI","active":true,"usgs":false}],"preferred":false,"id":844360,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70236449,"text":"70236449 - 2022 - Vadose zone thickness limits pore-fluid pressures and acceleration in a large, slow-moving landslide","interactions":[],"lastModifiedDate":"2022-09-07T11:59:46.590963","indexId":"70236449","displayToPublicDate":"2022-06-10T06:56:29","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5739,"text":"Journal of Geophysical Research: Earth Surface","onlineIssn":"2169-9011","active":true,"publicationSubtype":{"id":10}},"title":"Vadose zone thickness limits pore-fluid pressures and acceleration in a large, slow-moving landslide","docAbstract":"

The rate and timing of hydrologically forced landslides is a complex function of precipitation patterns, material properties, topography, and groundwater hydrology. In the simplest form, however, slopes fail when subsurface pore pressure grows large enough to exceed the Mohr-Coulomb failure criterion. The capacity for pore pressure rise in a landslide is determined in part by the thickness of the unsaturated zone above the water table, which itself is set by weathering patterns that should have predictable patterns across different lithologies. To investigate how this structure affects landslide behavior, we exploit a multi-year record of precipitation, pore pressure, and velocity from Oak Ridge earthflow, a slow-moving landslide set in Franciscan mélange, northern California, USA. In conjunction with electrical resistivity tomography and hydraulic conductivity measurements, these data show that Oak Ridge has a thin weathered profile that is comparable in thickness to other mélange landslides in California. We propose that due to the inherently thin vadose zone, mélange landscapes experience an unusually high water table that frequently brings them close to movement; however, the capacity to increase stress is limited by the small amount of dynamic storage available. Instead, excess pore pressure is shed via springs and saturation overland flow once the water table reaches the surface. Linkages between weathering patterns, hydrology, and deformation can explain behavior patterns exhibited by Franciscan mélange earthflows across a large precipitation gradient.

","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2021JF006415","usgsCitation":"Murphy, C., Finnegan, N., and Oberle, F.K., 2022, Vadose zone thickness limits pore-fluid pressures and acceleration in a large, slow-moving landslide: Journal of Geophysical Research: Earth Surface, v. 127, no. 6, e2021JF006415, 20 p., https://doi.org/10.1029/2021JF006415.","productDescription":"e2021JF006415, 20 p.","ipdsId":"IP-137805","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":447478,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2021jf006415","text":"Publisher Index Page"},{"id":406296,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -125.5517578125,\n 32.58384932565662\n ],\n [\n -118.0810546875,\n 32.58384932565662\n ],\n [\n -118.0810546875,\n 41.541477666790286\n ],\n [\n -125.5517578125,\n 41.541477666790286\n ],\n [\n -125.5517578125,\n 32.58384932565662\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"127","issue":"6","noUsgsAuthors":false,"publicationDate":"2022-06-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Murphy, C.R.","contributorId":296256,"corporation":false,"usgs":false,"family":"Murphy","given":"C.R.","email":"","affiliations":[{"id":17620,"text":"UCSC","active":true,"usgs":false}],"preferred":false,"id":851031,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Finnegan, N.J. 0000-0002-8505-6526","orcid":"https://orcid.org/0000-0002-8505-6526","contributorId":296258,"corporation":false,"usgs":false,"family":"Finnegan","given":"N.J.","email":"","affiliations":[{"id":17620,"text":"UCSC","active":true,"usgs":false}],"preferred":false,"id":851032,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Oberle, Ferdinand K.J. 0000-0001-8871-3619","orcid":"https://orcid.org/0000-0001-8871-3619","contributorId":214402,"corporation":false,"usgs":true,"family":"Oberle","given":"Ferdinand","middleInitial":"K.J.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":851033,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70238498,"text":"70238498 - 2022 - Targeting sagebrush (Artemisia spp.) restoration following wildfire with Greater Sage-Grouse (Centrocercus urophasianus) nest selection and survival models","interactions":[],"lastModifiedDate":"2022-11-28T12:19:06.34769","indexId":"70238498","displayToPublicDate":"2022-06-10T06:15:49","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1547,"text":"Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Targeting sagebrush (Artemisia spp.) restoration following wildfire with Greater Sage-Grouse (Centrocercus urophasianus) nest selection and survival models","docAbstract":"

Unprecedented conservation efforts for sagebrush (Artemisia spp.) ecosystems across the western United States have been catalyzed by risks from escalated wildfire activity that reduces habitat for sagebrush-obligate species such as Greater Sage-Grouse (Centrocercus urophasianus). However, post-fire restoration is challenged by spatial variation in ecosystem processes influencing resilience to disturbance and resistance to non-native invasive species, and spatial and temporal lags between slower sagebrush recovery processes and faster demographic responses of sage-grouse to loss of important habitat. Decision-support frameworks that account for these factors can help users strategically apply restoration efforts by predicting short and long-term ecological benefits of actions. Here, we developed a framework that strategically targets burned areas for restoration actions (e.g., seeding or planting sagebrush) that have the greatest potential to positively benefit sage-grouse populations through time. Specifically, we estimated sagebrush recovery following wildfire and risk of non-native annual grass invasion under four scenarios: passive recovery, grazing exclusion, active restoration with seeding, and active restoration with seedling transplants. We then applied spatial predictions of integrated nest site selection and survival models before wildfire, immediately following wildfire, and at 30 and 50 years post-wildfire based on each restoration scenario and measured changes in habitat. Application of this framework coupled with strategic planting designs aimed at developing patches of nesting habitat may help increase operational resilience for fire-impacted sagebrush ecosystems.

","language":"English","publisher":"Springer Nature","doi":"10.1007/s00267-022-01649-0","usgsCitation":"Roth, C.L., O’Neil, S.T., Coates, P.S., Ricca, M.A., Pyke, D.A., Aldridge, C.L., Heinrichs, J.A., Espinosa, S.P., and Delehanty, D.J., 2022, Targeting sagebrush (Artemisia spp.) restoration following wildfire with Greater Sage-Grouse (Centrocercus urophasianus) nest selection and survival models: Environmental Management, v. 70, p. 288-306, https://doi.org/10.1007/s00267-022-01649-0.","productDescription":"19 p.","startPage":"288","endPage":"306","ipdsId":"IP-123159","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":447481,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s00267-022-01649-0","text":"Publisher Index Page"},{"id":435809,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96K6X05","text":"USGS data release","linkHelpText":"Sagebrush Restoration Following Fire Disturbance in the Virginia Mountains, Nevada (2018)"},{"id":409662,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"70","noUsgsAuthors":false,"publicationDate":"2022-06-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Roth, Cali L. 0000-0001-9077-2765 croth@usgs.gov","orcid":"https://orcid.org/0000-0001-9077-2765","contributorId":174422,"corporation":false,"usgs":true,"family":"Roth","given":"Cali","email":"croth@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":857639,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"O’Neil, Shawn T. 0000-0002-0899-5220","orcid":"https://orcid.org/0000-0002-0899-5220","contributorId":206589,"corporation":false,"usgs":true,"family":"O’Neil","given":"Shawn","email":"","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":857640,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":857641,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ricca, Mark A. 0000-0003-1576-513X mark_ricca@usgs.gov","orcid":"https://orcid.org/0000-0003-1576-513X","contributorId":139103,"corporation":false,"usgs":true,"family":"Ricca","given":"Mark","email":"mark_ricca@usgs.gov","middleInitial":"A.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":857642,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pyke, David A. 0000-0002-4578-8335 david_a_pyke@usgs.gov","orcid":"https://orcid.org/0000-0002-4578-8335","contributorId":3118,"corporation":false,"usgs":true,"family":"Pyke","given":"David","email":"david_a_pyke@usgs.gov","middleInitial":"A.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":857643,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":857644,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Heinrichs, Julie A. 0000-0001-7733-5034 jheinrichs@usgs.gov","orcid":"https://orcid.org/0000-0001-7733-5034","contributorId":193742,"corporation":false,"usgs":true,"family":"Heinrichs","given":"Julie","email":"jheinrichs@usgs.gov","middleInitial":"A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":857645,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Espinosa, Shawn P.","contributorId":195583,"corporation":false,"usgs":false,"family":"Espinosa","given":"Shawn","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":857646,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Delehanty, David J.","contributorId":195584,"corporation":false,"usgs":false,"family":"Delehanty","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":857647,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70262475,"text":"70262475 - 2022 - The occupancy-abundance relationship and sampling designs using occupancy to monitor populations of Asian bears","interactions":[],"lastModifiedDate":"2025-01-22T15:30:34.089451","indexId":"70262475","displayToPublicDate":"2022-06-10T00:00:00","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"The occupancy-abundance relationship and sampling designs using occupancy to monitor populations of Asian bears","docAbstract":"

Designing a population monitoring program for Asian bears presents challenges associated with their low densities and detectability, generally large home ranges, and logistical or resource constraints. The use of an occupancy-based method to monitor bear populations can be appropriate under certain conditions given the mechanistic relationship between occupancy and abundance. The form of the occupancy–abundance relationship is dependent on species-specific characteristics such as home range size and population density, as well as study area size. To assess the statistical power of tests to detect population change of Asian bears, we conducted a study using a range of scenarios by simulating spatially explicit individual-based capture-recapture data from a demographically open model. Simulations assessed the power to detect changes in population density via changes in site-level occupancy or abundance through time, estimated using a standard occupancy model or a Royle-Nichols model, both with point detectors (representing camera traps). We used IUCN Red List criteria as a guide in selection of two population decline scenarios (20% and 50%), but we chose a shorter time horizon (10 years = 1 bear generation), meaning that declines were steeper than used for IUCN criteria (3 generations). Our simulations detected population declines of 50% with high power (>0.80) and low false positive rates (FPR: incorrectly detecting a decline) (<0.10) when detectors were spaced at > 0.67 times the home range diameter (home-range spacing ratio: HRSR, a measure of spatial correlation), such that bears would tend to overlap no more than two detectors. There was high (0.85) correlation between realized occupancy and N in these scenarios. The FPR increased as the HRSR decreased because of spatial correlation in the occupancy process induced when individual home ranges overlap multiple detectors. The mean statistical power to detect more gradual population declines (20% in 10 years) with HRSR > 0.67 was low for occupancy models 0.22 (maximum power 0.67) and Royle-Nichols models (0.24; maximum power 0.67), suggesting that declines of this magnitude may not be described reliably with 10 years of monitoring. Our results demonstrated that under many realistic scenarios that we explored, false positive rates were unacceptably high. We highlight that when designing occupancy studies, the spacing between point detectors be at least 0.67 times the diameter of the home range size of the larger sex (e.g., males) when the assumptions of the spatial capture-recapture model used for simulation are met.

","language":"English","publisher":"Elsevier","doi":"10.1016/j.gecco.2022.e02075","usgsCitation":"Fuller, A.K., Augustine, B., Morin, D., Pigeon, K., Boulanger, J., Lee, D., Bisi, F., and Garshelis, D., 2022, The occupancy-abundance relationship and sampling designs using occupancy to monitor populations of Asian bears: Global Ecology and Conservation, v. 35, e02075, 18 p., https://doi.org/10.1016/j.gecco.2022.e02075.","productDescription":"e02075, 18 p.","ipdsId":"IP-135705","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481083,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2022.e02075","text":"Publisher Index Page"},{"id":480920,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"35","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Fuller, Angela K. 0000-0002-9247-7468 afuller@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-7468","contributorId":3984,"corporation":false,"usgs":true,"family":"Fuller","given":"Angela","email":"afuller@usgs.gov","middleInitial":"K.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":924297,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Augustine, Ben C.","contributorId":349417,"corporation":false,"usgs":false,"family":"Augustine","given":"Ben C.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":924298,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Morin, Dana J.","contributorId":349419,"corporation":false,"usgs":false,"family":"Morin","given":"Dana J.","affiliations":[{"id":17848,"text":"Mississippi State University","active":true,"usgs":false}],"preferred":false,"id":924299,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pigeon, Karine","contributorId":349420,"corporation":false,"usgs":false,"family":"Pigeon","given":"Karine","affiliations":[{"id":83340,"text":"IUCN SSC Bear Specialist Group","active":true,"usgs":false}],"preferred":false,"id":924300,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Boulanger, John","contributorId":349422,"corporation":false,"usgs":false,"family":"Boulanger","given":"John","affiliations":[{"id":83347,"text":"Integrated Ecological Research","active":true,"usgs":false}],"preferred":false,"id":924301,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Lee, David C.","contributorId":349424,"corporation":false,"usgs":false,"family":"Lee","given":"David C.","affiliations":[{"id":83348,"text":"University of South Wales","active":true,"usgs":false}],"preferred":false,"id":924302,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bisi, Francesco","contributorId":349429,"corporation":false,"usgs":false,"family":"Bisi","given":"Francesco","affiliations":[{"id":83482,"text":"University of Insubria","active":true,"usgs":false}],"preferred":false,"id":924303,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Garshelis, David L.","contributorId":349431,"corporation":false,"usgs":false,"family":"Garshelis","given":"David L.","affiliations":[{"id":83484,"text":"IUCN SSC Bear Specialist Group.","active":true,"usgs":false}],"preferred":false,"id":924304,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70232181,"text":"ofr20221055 - 2022 - Belowground mutualisms to support prairie reconstruction—Improving prairie habitat using mycorrhizal inoculum","interactions":[],"lastModifiedDate":"2022-09-27T12:39:15.078463","indexId":"ofr20221055","displayToPublicDate":"2022-06-09T13:18:15","publicationYear":"2022","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2022-1055","displayTitle":"Belowground Mutualisms to Support Prairie Reconstruction—Improving Prairie Habitat Using Mycorrhizal Inoculum","title":"Belowground mutualisms to support prairie reconstruction—Improving prairie habitat using mycorrhizal inoculum","docAbstract":"

As a first step toward understanding the feasibility of using arbuscular mycorrhizal fungi (AMF) in reconstruction practice, we addressed four objectives: (1) compare root-associated AMF communities of plants between high-quality remnant prairies and reconstructed prairies, (2) compare root-associated AMF communities between plant species that declined in reconstructions and species that were thriving, (3) compare AMF communities collected from roots of plants in geographically separate parts of Minnesota and Iowa, and (4) assess the relationship between AMF communities and soil abiotic factors. We collected soil and root samples in 8 prairies reconstructed in 2005 (and monitored through 2015) and 6 remnant prairies, and the samples were separated into 6 geographically determined clusters, each containing 1–2 reconstructions and 1 remnant. Sequencing was completed on 1,188 deoxyribonucleic acid extracts from individual plant root samples, and fungal sequences were clustered to operational taxonomic units at 97-percent identity. Nonmetric multidimensional scaling was used to visualize differences in species composition of AMF communities among plant species and field sites. Permutational analysis of variance was completed to test for differences in AMF community composition between the 2 types of sites (remnants and reconstructions), among plant species, and among the 6 site clusters. AMF communities differed between remnant and reconstructed prairies, with one exception, and AMF associated with individual plant species also tended to differ, depending on whether the plant species’ roots were collected from remnant or reconstructed prairie. On the other hand, we did not determine that, as a group, species in decline in the reconstructions we had monitored were more likely to harbor different AMF communities compared to species not in decline in the reconstructions. Significant interactions between site type and clusters indicate geographic variation in AMF communities. Total carbon and nitrogen, and organic matter, were higher in remnant soils, whereas phosphorus, which at high concentrations reduces the value of AMF to plants, was much higher in soils collected from reconstructions.

","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221055","collaboration":"Prepared in cooperation with U.S. Fish and Wildlife Service, North Dakota State University, University of Minnesota, and University of Groningen","programNote":"Land Management Research Program","usgsCitation":"Vink, S.N., Aldrich-Wolfe, L., Huerd, S.C., Larson, J.L., Vacek, S.C., Drobney, P.M., Barnes, M., Viste-Sparkman, K., Jordan, N.R., and Larson, D.L., 2022, Belowground mutualisms to support prairie reconstruction—Improving prairie habitat using mycorrhizal inoculum: U.S. Geological Survey Open-File Report 2022–1055, 18 p., https://doi.org/10.3133/ofr20221055.","productDescription":"Report: vi, 18 p.; 2 Data Releases","numberOfPages":"28","onlineOnly":"Y","ipdsId":"IP-138435","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":402010,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221055/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":402009,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95R5UNN","text":"USGS data release","linkHelpText":"Arbuscular mycorrhizal fungi in remnant and reconstructed prairies in Minnesota and Iowa, 2019 (ver. 2.0, April 2022)"},{"id":402007,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1055/images"},{"id":402006,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1055/ofr20221055.XML"},{"id":402005,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1055/ofr20221055.pdf","text":"Report","size":"1.18 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1055"},{"id":402004,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1055/coverthb.jpg"},{"id":402008,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N8X0ZY","text":"USGS data release","linkHelpText":"Management of remnant tallgrass prairie by grazing or fire in western Minnesota, 2016–2017"}],"country":"United States","state":"Iowa, Minnesota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.26220703125,\n 46.37725420510028\n ],\n [\n -94.81201171875,\n 46.37725420510028\n ],\n [\n -94.81201171875,\n 47.931066347509784\n ],\n [\n -96.26220703125,\n 47.931066347509784\n ],\n [\n -96.26220703125,\n 46.37725420510028\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -96.767578125,\n 44.5278427984555\n ],\n [\n -94.28466796874999,\n 44.5278427984555\n ],\n [\n -94.28466796874999,\n 45.706179285330855\n ],\n [\n -96.767578125,\n 45.706179285330855\n ],\n [\n -96.767578125,\n 44.5278427984555\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.93310546874999,\n 40.9964840143779\n ],\n [\n -91.91162109374997,\n 40.9964840143779\n ],\n [\n -91.91162109374997,\n 41.78769700539063\n ],\n [\n -93.93310546874999,\n 41.78769700539063\n ],\n [\n -93.93310546874999,\n 40.9964840143779\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"

Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401

Contact Pubs Warehouse

","tableOfContents":"","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2022-06-09","noUsgsAuthors":false,"publicationDate":"2022-06-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Vink, Stefanie N.","contributorId":292411,"corporation":false,"usgs":false,"family":"Vink","given":"Stefanie","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":844473,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aldrich-Wolfe, Laura","contributorId":49263,"corporation":false,"usgs":true,"family":"Aldrich-Wolfe","given":"Laura","email":"","affiliations":[],"preferred":false,"id":844474,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Huerd, Sheri C.","contributorId":56901,"corporation":false,"usgs":true,"family":"Huerd","given":"Sheri","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":844475,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Larson, Jennifer L 0000-0002-6259-0101","orcid":"https://orcid.org/0000-0002-6259-0101","contributorId":257024,"corporation":false,"usgs":true,"family":"Larson","given":"Jennifer","email":"","middleInitial":"L","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":844476,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sara C. Vacek","contributorId":149091,"corporation":false,"usgs":false,"family":"Sara C. Vacek","affiliations":[{"id":17638,"text":"U.S. Fish and Wildlife Service, Morris Wetland Management District","active":true,"usgs":false}],"preferred":false,"id":844477,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Drobney, Pauline M.","contributorId":292412,"corporation":false,"usgs":false,"family":"Drobney","given":"Pauline","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":844478,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Barnes, Marsha","contributorId":292413,"corporation":false,"usgs":false,"family":"Barnes","given":"Marsha","email":"","affiliations":[],"preferred":false,"id":844479,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Viste-Sparkman, Karen","contributorId":197593,"corporation":false,"usgs":false,"family":"Viste-Sparkman","given":"Karen","email":"","affiliations":[],"preferred":false,"id":844480,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Jordan, Nicholas R.","contributorId":39629,"corporation":false,"usgs":true,"family":"Jordan","given":"Nicholas","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":844481,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Larson, Diane L. 0000-0001-5202-0634 dlarson@usgs.gov","orcid":"https://orcid.org/0000-0001-5202-0634","contributorId":2120,"corporation":false,"usgs":true,"family":"Larson","given":"Diane","email":"dlarson@usgs.gov","middleInitial":"L.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":844482,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70232217,"text":"70232217 - 2022 - A lesser scaup (Aythya affinis ) naturally infected with Eurasian 2.3.4.4 highly pathogenic H5N1 avian influenza virus – Movement ecology and host factors","interactions":[],"lastModifiedDate":"2022-09-27T16:46:26.038359","indexId":"70232217","displayToPublicDate":"2022-06-09T09:23:48","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3849,"text":"Transboundary and Emerging Diseases","active":true,"publicationSubtype":{"id":10}},"displayTitle":"A lesser scaup (Aythya affinis ) naturally infected with Eurasian 2.3.4.4 highly pathogenic H5N1 avian influenza virus – Movement ecology and host factors","title":"A lesser scaup (Aythya affinis ) naturally infected with Eurasian 2.3.4.4 highly pathogenic H5N1 avian influenza virus – Movement ecology and host factors","docAbstract":"

Despite the recognized role of wild waterfowl in the potential dispersal and transmission of highly pathogenic avian influenza (HPAI) virus, little is known about how infection affects these birds. This lack of information limits our ability to estimate viral spread in the event of an HPAI outbreak, thereby limiting our abilities to estimate and communicate risk. Here we present telemetry data from a wild Lesser Scaup (Aythya affinis), captured during a separate ecology study in the Chesapeake Bay, Maryland. This bird tested positive for infection with clade 2.3.4.4 HPAI virus of the A/goose/Guangdong/1/1996 (Gs/GD) H5N1 lineage (results received post-release) during the 2021–22 ongoing outbreaks in North America. While the infected bird was somewhat lighter than other adult males surgically implanted with transmitters (790g, ߂ = 868g, n = 11), it showed no clinical signs of infection at capture, during surgery, nor upon release. The bird died 3d later, pathology undetermined as the specimen was not able to be recovered. Analysis of movement data within the 3d window showed that the infected individual's maximum and average hourly movements (3894.3m, 428.8m respectively) were noticeably lower than noninfected conspecifics tagged and released the same day (߂ = 21594.5m, ߂ = 1097.9m, respectively; n = 4). We identified four instances where the infected bird had close contact (fixes located within 25m and 15 min) with another marked bird during this time. Collectively, these data suggest that the HPAI positive bird observed in this study may have been shedding virus for some period prior to death, with opportunities for direct bird to bird or environmental transmission. Although limited by low sample size and proximity to the time of tagging, we hope that these data will provide useful information as managers continue to respond to this ongoing outbreak event.

","language":"English","publisher":"Wiley","doi":"10.1111/tbed.14614","usgsCitation":"Prosser, D., Schley, H., Simmons, N., Sullivan, J.D., Homyack, J., Weegman, M.M., Olsen, G.H., Berlin, A., Poulson, R., Stallknecht, D., and Williams, C.K., 2022, A lesser scaup (Aythya affinis ) naturally infected with Eurasian 2.3.4.4 highly pathogenic H5N1 avian influenza virus – Movement ecology and host factors: Transboundary and Emerging Diseases, v. 69, no. 5, p. e2653-e2660, https://doi.org/10.1111/tbed.14614.","productDescription":"8 p.","startPage":"e2653","endPage":"e2660","ipdsId":"IP-138877","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":435810,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MJG53M","text":"USGS data release","linkHelpText":"Telemetry data of a Lesser Scaup (Aythya affinis) positive for 2.3.4.4 Highly Pathogenic 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ecosystems throughout Eurasia leaves central Asia with some of the world’s last remaining large expanses of grassland habitat. Steppe eagles (Aquila nipalensis) are globally endangered breed primarily in these steppe ecosystems. We evaluated migratory movements of first year steppe eagles hatched in northern Kazakhstan, to understand their migration and the extent to which their movements expose them to threats that may impact population viability. Most steppe eagles we monitored migrated to the east of the Caspian Sea to wintering grounds on the Arabian Peninsula or northeastern Africa, although a few migrated to the west of the Caspian Sea, one wintered in southcentral Iran, and one in southern Pakistan. Northbound migration routes largely mirrored southbound routes. Straight-line distance between summering and wintering grounds averaged 3,582 km (fall) and 3,700 km (spring), and actual distance traveled averaged 7,183 km (fall) and 9,433 km (spring). Routes of travel of these steppe eagles expose them to potential electrocution, shooting, and wildlife trade across the extent of their migratory and wintering areas.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Biological diversity of Asian steppes","largerWorkSubtype":{"id":12,"text":"Conference publication"},"conferenceTitle":"IV International Scientific Conference","conferenceDate":"April 14, 2022","conferenceLocation":"Kostanay, Kazakhstan","language":"English","publisher":"A. Baitursynov Kostanay Regional University","usgsCitation":"Katzner, T., Efrat, R., Bragin, A.E., Lehnardt, Y., Bragin, E.A., and Sapir, N., 2022, Migration of first-year steppe eagles (Aquila nipalensis) from northern Kazakhstan and implications for conservation, in Biological diversity of Asian steppes, Kostanay, Kazakhstan, April 14, 2022, p. 310-317.","productDescription":"8 p.","startPage":"310","endPage":"317","ipdsId":"IP-137747","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":401985,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":401984,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://ksu.edu.kz/about/biblioteka/elektronnye-resursy/"}],"country":"Afghanistan, Azerbaijan, Djibouti, Eritrea, Ethiopia, Iran, Iraq, Kazakhstan, Kuwait, Pakistan, Russia, Saudi Arabia, South Sudan, Sudan, Turkmenistan, Uzbekistan, Yemen","state":"Aktobe, Kostanay","otherGeospatial":"Caspian Sea, Persian Gulf","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 28.828124999999996,\n 10.14193168613103\n ],\n [\n 35.33203125,\n 5.528510525692801\n ],\n [\n 46.669921875,\n 13.838079936422462\n ],\n [\n 48.603515625,\n 29.611670115197377\n ],\n [\n 49.74609374999999,\n 29.6880527498568\n ],\n [\n 51.064453125,\n 27.761329874505233\n ],\n [\n 53.26171875,\n 26.588527147308614\n ],\n [\n 54.66796875,\n 25.958044673317843\n ],\n [\n 56.42578125,\n 26.509904531413927\n ],\n [\n 57.041015625,\n 25.48295117535531\n ],\n [\n 61.69921875,\n 24.766784522874453\n ],\n [\n 66.005859375,\n 25.16517336866393\n ],\n [\n 68.994140625,\n 23.483400654325642\n ],\n [\n 82.353515625,\n 47.100044694025215\n ],\n [\n 70.83984375,\n 54.77534585936447\n ],\n [\n 39.63867187499999,\n 47.39834920035926\n ],\n [\n 44.033203125,\n 37.23032838760387\n ],\n [\n 30.937499999999996,\n 22.024545601240337\n ],\n [\n 28.828124999999996,\n 10.14193168613103\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":844415,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Efrat, R.","contributorId":292407,"corporation":false,"usgs":false,"family":"Efrat","given":"R.","affiliations":[{"id":36498,"text":"Ben-Gurion University of the Negev","active":true,"usgs":false}],"preferred":false,"id":844468,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bragin, A. 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