The invasive American bullfrog (Lithobates catesbeianus) and a variety of non‐native sport fish commonly co‐occur in lowland lentic habitats of the western United States. Both invasive taxa are implicated in declines of native amphibians in this region, but few long‐term studies of communities exist. Further, field studies of invasive–native interactions are complicated by confounding habitat modifications and observation errors. We surveyed amphibians and measured habitat characteristics for 12 yr across 38 wetland sites within the Willamette Valley, Oregon, USA. We assessed the influence of invasive species, habitat, and their interactions on the distributions of five native amphibian species using a multispecies dynamic occupancy model that accounted for false‐negative and false‐positive detections. In general, habitat characteristics—such as within‐pond vegetation cover, surrounding forest, and drought severity—were important for local persistence of native species when bullfrogs co‐occurred. We also found evidence of a cumulative negative effect of bullfrogs and non‐native fish (families Centrarchidae and Ictaluridae) on northern red‐legged frog (Rana aurora) local persistence that was mediated by the dominance of invasive reed canarygrass (Phalaris arundinacea). Non‐native fish and bullfrogs had variable effects on native amphibian species, but neither invasive taxon appears to be causing declines in occupied sites within our study area. Moreover, species relationships with habitat differed when invaders were present, indicating that certain habitats may increase persistence of native amphibians in the invaded landscape.
","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.2674","usgsCitation":"Rowe, J., Duarte, A., Pearl, C., McCreary, B., Galvan, S., Peterson, J.T., and Adams, M.J., 2019, Disentangling effects of invasive species and habitat while accounting for observer error in a long-term amphibian study: Ecosphere, v. 10, no. 4, e02674, 22 p., https://doi.org/10.1002/ecs2.2674.","productDescription":"e02674, 22 p.","ipdsId":"IP-098966","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":460417,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2674","text":"Publisher Index Page"},{"id":365327,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Willamette Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.431396484375,\n 44.15856343854312\n ],\n [\n -122.7447509765625,\n 44.15856343854312\n ],\n [\n -122.7447509765625,\n 45.41002023463975\n ],\n [\n -123.431396484375,\n 45.41002023463975\n ],\n [\n -123.431396484375,\n 44.15856343854312\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"10","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2019-04-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Rowe, Jennifer 0000-0002-5253-2223 jrowe@usgs.gov","orcid":"https://orcid.org/0000-0002-5253-2223","contributorId":172670,"corporation":false,"usgs":true,"family":"Rowe","given":"Jennifer","email":"jrowe@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":765576,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duarte, Adam","contributorId":28492,"corporation":false,"usgs":false,"family":"Duarte","given":"Adam","affiliations":[{"id":6960,"text":"Department of Biology, Texas State University","active":true,"usgs":false}],"preferred":false,"id":765577,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pearl, Christopher 0000-0003-2943-7321 christopher_pearl@usgs.gov","orcid":"https://orcid.org/0000-0003-2943-7321","contributorId":172669,"corporation":false,"usgs":true,"family":"Pearl","given":"Christopher","email":"christopher_pearl@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":765578,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McCreary, Brome 0000-0002-0313-7796 brome_mccreary@usgs.gov","orcid":"https://orcid.org/0000-0002-0313-7796","contributorId":3130,"corporation":false,"usgs":true,"family":"McCreary","given":"Brome","email":"brome_mccreary@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":765579,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Galvan, Stephanie 0000-0002-9864-3674 stephanie_galvan@usgs.gov","orcid":"https://orcid.org/0000-0002-9864-3674","contributorId":3135,"corporation":false,"usgs":true,"family":"Galvan","given":"Stephanie","email":"stephanie_galvan@usgs.gov","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":765580,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Peterson, James T. 0000-0002-7709-8590","orcid":"https://orcid.org/0000-0002-7709-8590","contributorId":204948,"corporation":false,"usgs":false,"family":"Peterson","given":"James","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":765581,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Adams, Michael J. 0000-0001-8844-042X","orcid":"https://orcid.org/0000-0001-8844-042X","contributorId":211916,"corporation":false,"usgs":true,"family":"Adams","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":765582,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70202847,"text":"ds1112 - 2019 - Terrestrial lidar data of the February 14, 2019 Sausalito Boulevard Landslide, Sausalito, California","interactions":[],"lastModifiedDate":"2019-04-05T14:46:46","indexId":"ds1112","displayToPublicDate":"2019-04-02T08:23:34","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1112","displayTitle":"Terrestrial Lidar Data of the February 14, 2019, Sausalito Boulevard Landslide, Sausalito, California","title":"Terrestrial lidar data of the February 14, 2019 Sausalito Boulevard Landslide, Sausalito, California","docAbstract":"On February 14, 2019, just before 2:56 am local time (Pacific Standard Time), a landslide initiated from the hillslopes above the Hurricane Gulch section of the City of Sausalito, Marin County, California. The landslide, specifically classified as a debris flow, overran a road (Sausalito Boulevard) immediately below the landslide source area and impacted a residential structure that subsequently toppled downslope and collided with another residential structure. The second structure then crossed a lower road (Crescent Avenue) that runs along the base of the slope before the mixture of soil and structural debris came to rest in and near the valley axis that drains the lower area of Hurricane Gulch.
The U.S. Geological Survey responded to this event within hours of the landslide and provided situational awareness of possible secondary landslide hazards associated with the unstable slope. The USGS also rapidly mobilized its topographic surveying capabilities (specifically, GPS and terrestrial lidar devices) and collected a three-dimensional point cloud model of the landslide source area and surrounding terrain to capture the as-failed condition of the slope for use in potential future studies. This report summarizes the methods and available data collected during this response.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1112","usgsCitation":"Collins, B.D. and Corbett, S.C., 2019. Terrestrial lidar data of the February 14, 2019 Sausalito Boulevard Landslide, Sausalito, California: U.S. Geological Survey Data Series 1112, 12 p., https://doi.org/10.3133/ds1112.","productDescription":"Report: iv, 12 p.; Data Release","numberOfPages":"19","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-106637","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":362641,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AQRCTJ","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Terrestrial LIDAR Data Set of the February 14, 2019 Sausalito Boulevard Landslide, Sausalito, California"},{"id":362639,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1112/coverthb.jpg"},{"id":362640,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1112/ds1112_.pdf","text":"Report","size":"34.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"DS 1112"}],"country":"United States","state":"California","city":"Sausalito","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.5074291229248,\n 37.87376937332855\n ],\n [\n -122.50828742980956,\n 37.86299605572604\n ],\n [\n -122.48854637145995,\n 37.84422368363511\n ],\n [\n -122.47670173645018,\n 37.84564702731293\n ],\n [\n -122.47756004333496,\n 37.859540129644195\n ],\n [\n -122.50288009643553,\n 37.87715688349197\n ],\n [\n -122.5074291229248,\n 37.87376937332855\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Geology, Minerals, Energy, & Geophysics Science Center—Menlo Park
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
Earthquake swarms present a challenge for operational earthquake forecasting because they are driven primarily by transient external processes, such as fluid flow, the behavior and duration of which are difficult to predict. In this study, we develop a swarm duration model to estimate how long a swarm is likely to last based on actuarial statistics of previous swarms in a given region. We demonstrate this approach using swarms that have been identified in the Salton trough in southern California, finding that swarms last an average of
Situated in the Mississippi Alluvial Plain of the Gulf Coast Prairie Landscape Conservation Cooperative (GCP LCC), the Chitimacha Tribe is one of four federally recognized tribes in Louisiana. The Tribal seat, trust lands/ reservation, and adjacent Tribal owned lands are located near Charenton, Louisiana, totaling nearly 1,000 acres. The Chitimacha, with a population of approximately 1,400 people, are currently impacted by storm surge, which is expected to increase with climate change. The additional stress from storms will likewise increase the vulnerability to catastrophic impact in the event of a breach in the Atchafalaya Basin Spillway levee. A collaborative effort between the U.S. Geological Survey (USGS) and the Chitimacha Tribe has been initiated to provide resources and expertise to increase the Tribe’s ability to prevent, plan, and prepare for these environmental challenges. By enhancing technical skills, providing access to environmental data, and increasing awareness of environmental issues, the Chitimacha will be better prepared to plan and adapt to the environmental impacts facing their lands related to land use and climate change.
For this project, USGS researchers asked how Chitimacha Tribal Lands might be impacted by future sea level rise scenario projections. These models illustrate some flooding within the northernmost boundary of Chitimacha Tribal Lands.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191030","collaboration":"Prepared in cooperation with The Chitimacha Tribe of Louisiana; Gulf Coast Prairie Landscape Conservation Cooperative","usgsCitation":"Spear, K.A., Jones, W., Griffith, K., Tirpak, B.E., and Walden, K., 2019, Potential sea level rise on Chitimacha Tribal Lands in Louisiana: U.S. Geological Survey Open-File Report 2019–1030, 1 sheet, https://doi.org/10.3133/ofr20191030.","productDescription":"1 Sheet: 24.0 x 36.0 inches","onlineOnly":"Y","ipdsId":"IP-090045","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":362386,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1030/coverthb.jpg"},{"id":362387,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1030/ofr20191030.pdf","text":"Report","size":"1.88 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019–1030"}],"country":"United States","state":"Louisiana","otherGeospatial":"Chitimacha Tribal Lands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.5833,\n 29.8167\n ],\n [\n -91.5,\n 29.8167\n ],\n [\n -91.5,\n 29.9167\n ],\n [\n -91.5833,\n 29.9167\n ],\n [\n -91.5833,\n 29.8167\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wetland and Aquatic Research Center
U.S. Geological Survey
700 Cajundome Blvd.
Lafayette, LA 70506
Recently introduced unmarked spatial capture–recapture (SCR), spatial mark–resight (SMR), and 2‐flank spatial partial identity models (SPIMs) extend the domain of SCR to populations or observation systems that do not always allow for individual identity to be determined with certainty. For example, some species do not have natural marks that can reliably produce individual identities from photographs, and some methods of observation produce partial identity samples as is the case with remote cameras that sometimes produce single‐flank photographs. Unmarked SCR, SMR, and SPIM share the feature that they probabilistically resolve the uncertainty in individual identity using the spatial location where samples were collected. Spatial location is informative of individual identity in spatially structured populations because a sample is more likely to have been produced by an individual living near the trap where it was recorded than an individual living further away from the trap. Further, the level of information about individual identity that a spatial location contains is related to two key ecological concepts, population density and home range size, which we quantify using a proposed Identity Diversity Index (IDI). We show that latent and partial identity SCR models produce imprecise and biased density estimates in many high IDI scenarios when data are sparse. We then extend the unmarked SCR model to incorporate categorical, partially identifying covariates, which reduce the level of uncertainty in individual identity, increasing the reliability and precision of density estimates, and allowing reliable density estimation in scenarios with higher IDI values and with more sparse data. We illustrate the performance of this “categorical SPIM” via simulations and by applying it to a black bear data set using microsatellite loci as categorical covariates, where we reproduce the full data set estimates with only slightly less precision using fewer loci than necessary for confident individual identification. We then discuss how the categorical SPIM can be applied to other wildlife sampling scenarios such as remote camera surveys, where natural or researcher‐applied partial marks can be observed in photographs. Finally, we discuss how the categorical SPIM can be added to SMR, 2‐flank SPIM, or other latent identity SCR models.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.2627","usgsCitation":"Augustine, B., Royle, J.A., Murphy, S.M., Chandler, R.B., Cox, J., and Kelly, M., 2019, Spatial capture–recapture for categorically marked populations with an application to genetic capture–recapture: Ecosphere, v. 10, no. 4, e02627, 22 p., https://doi.org/10.1002/ecs2.2627.","productDescription":"e02627, 22 p.","ipdsId":"IP-095923","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":467740,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.2627","text":"Publisher Index Page"},{"id":377199,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","issue":"4","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Augustine, Ben C.","contributorId":237797,"corporation":false,"usgs":false,"family":"Augustine","given":"Ben C.","affiliations":[{"id":38081,"text":"Cornell Univ.","active":true,"usgs":false}],"preferred":false,"id":795329,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":139626,"corporation":false,"usgs":true,"family":"Royle","given":"J.","email":"aroyle@usgs.gov","middleInitial":"Andrew","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":795331,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Murphy, Sean M.","contributorId":140195,"corporation":false,"usgs":false,"family":"Murphy","given":"Sean","email":"","middleInitial":"M.","affiliations":[{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":795330,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chandler, Richard B. rchandler@usgs.gov","contributorId":63524,"corporation":false,"usgs":true,"family":"Chandler","given":"Richard","email":"rchandler@usgs.gov","middleInitial":"B.","affiliations":[],"preferred":false,"id":795332,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cox, John J.","contributorId":140196,"corporation":false,"usgs":false,"family":"Cox","given":"John J.","affiliations":[{"id":12425,"text":"University of Kentucky","active":true,"usgs":false}],"preferred":false,"id":795334,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kelly, Marcella","contributorId":237800,"corporation":false,"usgs":false,"family":"Kelly","given":"Marcella","email":"","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":795333,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70204687,"text":"70204687 - 2019 - Quaternary eolian sediments and Carolina Bays of the U.S. Atlantic Coastal Plain province","interactions":[],"lastModifiedDate":"2019-08-08T15:01:05","indexId":"70204687","displayToPublicDate":"2019-04-01T14:57:01","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":18,"text":"Abstract or summary"},"title":"Quaternary eolian sediments and Carolina Bays of the U.S. Atlantic Coastal Plain province","docAbstract":"Under modern conditions, the Atlantic Coastal Plain province of the eastern United States is not very conducive to widespread eolian sediment mobilization because of a humid and mesothermal climate, relatively low mean surface wind velocities (~1–3 m/sec), and relatively dense vegetation. LiDAR data, however, have revealed the presence of widespread eolian dunes and sand sheets (now covered by vegetation) at many inland locations throughout the U.S. Atlantic Coastal Plain (Swezey, in press). To date, a total of 89 OSL ages ranging from ~92–5 thousand years ago (ka) have been published from these eolian sediments, and 61 of these 89 OSL ages occur within or near the interval of the last glacial maximum (LGM). \nQuaternary eolian sediments have been identified in the following four inland settings of the U.S. Atlantic Coastal Plain: (1) on interfluvial upland areas of the northern coastal plain; (2) in the Carolina Sandhills region; (3) within river valleys; and (4) adjacent to low relief elliptical depressions known as Carolina Bays. Most of these eolian sediments are composed of fine to medium quartz sand, although a substantial component of silt is present in the northern coastal plain, and a substantial component of coarse sand is present in the Carolina Sandhills region. \nThe eolian sediments in interfluvial upland areas of the northern coastal plain (Delaware, Maryland) form both sand sheets and parabolic dunes (with dune tails pointing to the northwest). These eolian sediments in the northern upland areas were probably remobilized from any loose sediments that were available in the area, and the location near the southern margin of the LGM ice sheet is similar to extensive Quaternary eolian sand and loess deposits in Europe, China, and the central United States. \nThe eolian sediments in the Carolina Sandhills region form mostly sand sheets and some linear dunes of relatively short extent. These eolian sediments are thought to have been derived from sand of the immediately underlying Cretaceous fluvial strata. \nThe eolian sediments within river valleys form parabolic dunes that are located to the east of the modern river channels. The tails of these eolian dunes within river valleys point northwest in the northern coastal plain (Delaware, Maryland) and they point west in the southern coastal plain (North Carolina, South Carolina, Georgia). These eolian sediments within river valleys are thought to have been derived from fluvial sand in the nearby river channels. \n\tThe eolian sediments associated with Carolina Bays form arcuate ridges on the east and south sides of the depressions (“bays”). Some Carolina Bays show cross-cutting relations with other Carolina Bays. Other Carolina Bays show different stratigraphic relations with respect to eolian dunes within river valleys. For example, Bear Swamp (Marion County, South Carolina) is a Carolina Bay that is inset into (i.e., younger than) eolian dunes in the valley of the Great Pee Dee River. As another example, Big Bay (Sumter County, South Carolina) is a Carolina Bay that is overlain by (i.e., older than) eolian dunes in the valley at the confluence of the Congaree and Wateree Rivers. Cores in Carolina Bays and their associated ridges reveal a few meters of sand and (or) muddy sand above an unconformity on various older fine-grained substrates that do not show signs of disturbance. Most published OSL ages from Carolina Bay sand ridges range from ~45–8 ka. Some bays have multiple sand ridges, and ridges closer to individual bays yield younger OSL ages. \nIn summary, Quaternary eolian sediments are widespread throughout the U.S. Atlantic Coastal Plain province, and most of these sediments are thought to have been mobilized within or near the interval of the LGM when conditions were much colder, drier, and windier. These eolian sediments are thus interpreted as relict features that have subsequently been stabilized and degraded by vegetation and pedoge","language":"English","publisher":"Minnesota Geological Survey","usgsCitation":"Swezey, C.S., 2019, Quaternary eolian sediments and Carolina Bays of the U.S. Atlantic Coastal Plain province, p. 88-89.","productDescription":"2 p.","startPage":"88","endPage":"89","ipdsId":"IP-105444","costCenters":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true}],"links":[{"id":366424,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":366412,"type":{"id":15,"text":"Index Page"},"url":"https://conservancy.umn.edu/handle/11299/202386"}],"country":"United States","state":"North Carolina, South Carolina 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Carolina\",\"nation\":\"USA \"}}]}","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Swezey, Christopher S. 0000-0003-4019-9264 cswezey@usgs.gov","orcid":"https://orcid.org/0000-0003-4019-9264","contributorId":173033,"corporation":false,"usgs":true,"family":"Swezey","given":"Christopher","email":"cswezey@usgs.gov","middleInitial":"S.","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},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":768071,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70204660,"text":"70204660 - 2019 - Individual based modelling of fish migration in a 2-D river system: Model description and case study","interactions":[],"lastModifiedDate":"2019-08-09T10:27:26","indexId":"70204660","displayToPublicDate":"2019-04-01T14:32:32","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2602,"text":"Landscape Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Individual based modelling of fish migration in a 2-D river system: Model description and case study","docAbstract":"Context: Diadromous fish populations in the Pacific Northwest face challenges along their migratory routes from declining habitat quality, harvest, and barriers to longitudinal connectivity. These stressors complicate the prioritization of proposed management actions intended to improve conditions for migratory fishes including anadromous salmon and trout.\n\nObjectives: We describe a multi-scale hybrid mechanistic–probabilistic simulation model linking migration corridor conditions to fish fitness outcomes. We demonstrate the model’s utility using a case study of salmon and steelhead adults in the Columbia River migration corridor exposed to spatially- and temporally-varying stressors.\n\nMethods: The migration corridor simulation model is based on a behavioral decision tree that governs individual interactions with the environment, and an energetic submodel that estimates the hourly costs of migration. Emergent properties of the migration corridor simulation model include passage time, energy use, and survival.\n\nResults: We observed that the simulated fish’s initial energy density, the migration corridor temperatures they experienced, and their history of behavioral thermoregulation were the primary determinants of their fitness outcomes. Insights gained from use of the model might be exploited to identify management interventions that increase successful migration outcomes.\n\nConclusions: This paper describes new methods that extend the suite of tools available to aquatic biologists and conservation practitioners. We have developed a 2-dimensional spatially-explicit behavioral and physiological model and illustrated how it can be used to simulate fish migration within a river system. Our model can be used to evaluate trade-offs between behavioral thermoregulation and fish fitness at population scales.","language":"English","publisher":"Springer","doi":"10.1007/s10980-019-00804-z","usgsCitation":"Snyder, M.N., Schumaker, N.H., Ebersole, J., Dunham, J.B., Comeleo, R., Keefer, M., Leinenbach, P., Brookes, A., Cope, B., Wu, J., Palmer, J., and Keenan, D., 2019, Individual based modelling of fish migration in a 2-D river system: Model description and case study: Landscape Ecology, v. 34, no. 4, p. 737-754, https://doi.org/10.1007/s10980-019-00804-z.","productDescription":"18 p.","startPage":"737","endPage":"754","ipdsId":"IP-106277","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":467741,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/7788051","text":"External Repository"},{"id":366419,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"34","issue":"4","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2019-03-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Snyder, Marcia N. 0000-0003-2202-2668","orcid":"https://orcid.org/0000-0003-2202-2668","contributorId":217972,"corporation":false,"usgs":false,"family":"Snyder","given":"Marcia","email":"","middleInitial":"N.","affiliations":[{"id":13529,"text":"US Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":767950,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schumaker, Nathan H.","contributorId":199151,"corporation":false,"usgs":false,"family":"Schumaker","given":"Nathan","email":"","middleInitial":"H.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":767951,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ebersole, Joseph E","contributorId":217973,"corporation":false,"usgs":false,"family":"Ebersole","given":"Joseph E","affiliations":[{"id":13529,"text":"US Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":767952,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dunham, Jason B. 0000-0002-6268-0633 jdunham@usgs.gov","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":147808,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","email":"jdunham@usgs.gov","middleInitial":"B.","affiliations":[{"id":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":767953,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Comeleo, Randy","contributorId":217974,"corporation":false,"usgs":false,"family":"Comeleo","given":"Randy","affiliations":[{"id":13529,"text":"US Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":767954,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Keefer, Matthew","contributorId":217975,"corporation":false,"usgs":false,"family":"Keefer","given":"Matthew","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":767955,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Leinenbach, P.T.","contributorId":217976,"corporation":false,"usgs":false,"family":"Leinenbach","given":"P.T.","affiliations":[{"id":13529,"text":"US Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":767956,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Brookes, Allen","contributorId":217977,"corporation":false,"usgs":false,"family":"Brookes","given":"Allen","email":"","affiliations":[{"id":13529,"text":"US Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":767957,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Cope, Ben","contributorId":217978,"corporation":false,"usgs":false,"family":"Cope","given":"Ben","email":"","affiliations":[{"id":13529,"text":"US Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":767958,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Wu, Jennifer","contributorId":217979,"corporation":false,"usgs":false,"family":"Wu","given":"Jennifer","email":"","affiliations":[{"id":13529,"text":"US Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":767959,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Palmer, John","contributorId":217980,"corporation":false,"usgs":false,"family":"Palmer","given":"John","email":"","affiliations":[{"id":13529,"text":"US Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":767960,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Keenan, Druscilla","contributorId":217981,"corporation":false,"usgs":false,"family":"Keenan","given":"Druscilla","affiliations":[{"id":13529,"text":"US Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":767961,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70203084,"text":"70203084 - 2019 - Early career climate communications and networking","interactions":[],"lastModifiedDate":"2020-07-27T19:03:54.947508","indexId":"70203084","displayToPublicDate":"2019-04-01T14:07:29","publicationYear":"2019","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":18,"text":"Abstract or summary"},"title":"Early career climate communications and networking","docAbstract":"The Department of the Interior and the U.S. Geological Survey have made it a priority to train the next generation of scientists and resource managers. The Climate Adaptation Science Centers (CSC) and consortium institutions are working to contribute to this initiative by supporting and building a network of students across the U.S. interested in the climate sciences and climate adaptation. The purpose of this project was to support the development of a national early career communication platform to facilitate and increase information sharing and networking across the CASCs and consortium institutions. This was accomplished by working with the Early Career Climate Forum (ECCF), a CASC-supported science network dedicated to improving research practice through communication and collaboration. Project goals included the redesign and expansion of a pilot website that was originally developed by early career scientists in attendance of the 2012 Northwest Climate Science Bootcamp as well as adding services such as a regular blog, email list-serve, database of members, training modules for CASC retreats and bootcamps, all directed towards the advancement of early career scientists in the field of climate change science and adaptation.","language":"English","usgsCitation":"Ezra Markowitz, and Staudinger, M., 2019, Early career climate communications and networking, 19 p.","productDescription":"19 p.","ipdsId":"IP-107130","costCenters":[{"id":41705,"text":"Northeast Climate Science Center","active":true,"usgs":true}],"links":[{"id":363907,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":363906,"rank":1,"type":{"id":11,"text":"Document"},"url":"https://necsc.umass.edu/sites/default/files/Final%20Report%20-Early%20Career%20Climate%20Communications%20and%20Networking.pdf"}],"publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ezra Markowitz","contributorId":214892,"corporation":false,"usgs":false,"family":"Ezra Markowitz","affiliations":[{"id":34616,"text":"University of Massachusetts Amherst","active":true,"usgs":false}],"preferred":false,"id":761096,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Staudinger, Michelle D. 0000-0002-4535-2005","orcid":"https://orcid.org/0000-0002-4535-2005","contributorId":207908,"corporation":false,"usgs":true,"family":"Staudinger","given":"Michelle D.","affiliations":[{"id":411,"text":"National Climate Change and Wildlife Science Center","active":true,"usgs":true},{"id":5080,"text":"Northeast Climate Adaptation Science Center","active":true,"usgs":true},{"id":484,"text":"Northwest Climate Science Center","active":true,"usgs":true}],"preferred":true,"id":761095,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70203376,"text":"70203376 - 2019 - Energetic costs of aquatic locomotion in a subadult polar bear","interactions":[],"lastModifiedDate":"2019-05-09T13:53:06","indexId":"70203376","displayToPublicDate":"2019-04-01T13:48:58","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2671,"text":"Marine Mammal Science","active":true,"publicationSubtype":{"id":10}},"title":"Energetic costs of aquatic locomotion in a subadult polar bear","docAbstract":"Most marine mammals rely on swimming as their primary form of locomotion. These animals have evolved specialized morphologies, physiologies, and behaviors that have enabled them to efficiently move through an aquatic environment (Williams 1999). Such adaptations include body streamlining, modified plantar surfaces for propulsion, and abilities to remain submerged for extended durations (Williams 1989). As a result of these adaptations, many marine mammal species exhibit minimal increases in metabolism at routine swim speeds relative to resting rates (Williams et al. 1992, 2017; Yazdi et al. 1999). Contrary to most marine mammals, polar bears (Ursus maritimus) rely on walking as their primary form of locomotion. As a consequence, they exhibit little evidence of body streamlining or abilities to remain submerged for extended durations. The longest dive recorded for a polar bear is 3 min and 10 s (Stirling and van Meurs 2015), a relatively brief period compared to other marine mammals (Ponganis 2015). Nevertheless, polar bears do exhibit large forepaws (DeMaster and Stirling 1981), lower and flatter heads (Slater et al. 2010), and more dense forelimb bones (Wall 1983) relative to other bear species, potentially as adaptations for swimming. Polar bears further exhibit some distinct physiological and behavioral adaptations from other bear species likely as a consequence of their marine existence (Pagano et al. 2018a).
","language":"English","publisher":"Society for Marine Mammalogy","doi":"10.1111/mms.12556","usgsCitation":"Pagano, A.M., Cutting, A., Nicassio-Hiskey, N., Hash, A., and Williams, T.M., 2019, Energetic costs of aquatic locomotion in a subadult polar bear: Marine Mammal Science, v. 35, no. 2, p. 649-659, https://doi.org/10.1111/mms.12556.","productDescription":"11 p.","startPage":"649","endPage":"659","ipdsId":"IP-098029","costCenters":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"links":[{"id":437516,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P98IQWT4","text":"USGS data release","linkHelpText":"Measures of oxygen consumption and stroke frequency of a captive subadult polar bear (Ursus maritimus) while resting in water and swimming and diving in a metabolic water flume, Oregon Zoo, 2017"},{"id":363649,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"35","issue":"2","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Pagano, Anthony M. 0000-0003-2176-0909 apagano@usgs.gov","orcid":"https://orcid.org/0000-0003-2176-0909","contributorId":3884,"corporation":false,"usgs":true,"family":"Pagano","given":"Anthony","email":"apagano@usgs.gov","middleInitial":"M.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":762382,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cutting, Amy","contributorId":200751,"corporation":false,"usgs":false,"family":"Cutting","given":"Amy","email":"","affiliations":[],"preferred":false,"id":762383,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nicassio-Hiskey, Nicole","contributorId":150616,"corporation":false,"usgs":false,"family":"Nicassio-Hiskey","given":"Nicole","email":"","affiliations":[{"id":18050,"text":"Oregon Zoo","active":true,"usgs":false}],"preferred":false,"id":762384,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hash, Amy","contributorId":200755,"corporation":false,"usgs":false,"family":"Hash","given":"Amy","email":"","affiliations":[],"preferred":false,"id":762385,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Williams, Terrie M.","contributorId":191735,"corporation":false,"usgs":false,"family":"Williams","given":"Terrie","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":762386,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70203001,"text":"70203001 - 2019 - Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data","interactions":[],"lastModifiedDate":"2019-04-11T13:46:12","indexId":"70203001","displayToPublicDate":"2019-04-01T13:45:11","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1760,"text":"Geoderma","active":true,"publicationSubtype":{"id":10}},"title":"Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data","docAbstract":"Fine scale maps of soil properties enable efficient land management and inform earth system models. Recent efforts to create soil property maps from field observations tend to use similar tree-based machine learning interpolation approaches, but often deal with depth of predictions, validation, and uncertainty differently. One of the main differences in approaches is whether to model individual depths of interest separately as ‘2D’ models, or to create models that incorporate depth as a predictor variable creating a ‘3D’ model that can make pre-dictions for all depths. It is unclear how choice of 2D or 3D approach influences model accuracy and uncertainty due to lack of direct comparison and inconsistent presentation of results in past studies. This study compares 2D and 3D methods for mapping soil electrical conductivity (salinity), pH, sum of fine and very fine sands, and organic carbon at 30 m resolution for the upper 432,000 km 2 of the Colorado River Watershed of the United States of America. A new, simple, model-agnostic relative prediction interval (RPI) approach to report un-certainty is presented that scales prediction interval width to the 95% interquantile width of the original training sample distribution. The RPI approach enables direct comparison of uncertainty between properties and depths and is easily interpretable by end users. Results indicate that 3D mapping of soil properties with strong variation with depth can result in substantial areas with much higher uncertainty that coincide with unrealistic predictions relative to 2D models, even though 3D models had slightly better global cross-validation scores. Maps and global model summaries of RPI proved helpful in identifying these issues with 3D models. These results suggest that the use of RPI or similar approaches to evaluate models can identify accuracy problems not evident in global va-lidation diagnostics.","language":"English","publisher":"ELsevier","doi":"10.1016/j.geoderma.2019.03.037","usgsCitation":"Nauman, T., and Duniway, M.C., 2019, Relative prediction intervals reveal larger uncertainty in 3D approaches to predictive digital soil mapping of soil properties with legacy data: Geoderma, v. 347, p. 170-184, https://doi.org/10.1016/j.geoderma.2019.03.037.","productDescription":"15 p.","startPage":"170","endPage":"184","ipdsId":"IP-102589","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":467743,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.geoderma.2019.03.037","text":"Publisher Index Page"},{"id":437517,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YBAKC2","text":"USGS data release","linkHelpText":"Predictive maps of 2D and 3D surface soil properties and associated uncertainty for the Upper Colorado River Basin, USA"},{"id":362917,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"347","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Nauman, Travis","contributorId":214769,"corporation":false,"usgs":true,"family":"Nauman","given":"Travis","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":760737,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":760738,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70202936,"text":"70202936 - 2019 - Using the value of information to improve conservation decision making","interactions":[],"lastModifiedDate":"2019-04-08T15:25:35","indexId":"70202936","displayToPublicDate":"2019-04-01T13:41:29","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1023,"text":"Biological Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Using the value of information to improve conservation decision making","docAbstract":"Conservation decisions are challenging, not only because they often involve difficult conflicts among outcomes that people value, but because our understanding of the natural world and our effects on it is fraught with uncertainty. Value of Information (VoI) methods provide an approach for understanding and managing uncertainty from the standpoint of the decision maker. These methods are commonly used in other fields (e.g., economics, public health) and are increasingly used in biodiversity conservation. This decision analytical approach can identify the best management alternative to select where the effectiveness of interventions is uncertain, and can help to decide when to act and when to delay action until after further research. We review the use of VoI in the environmental domain, reflect on the need for greater uptake of VoI, particularly for strategic conservation planning, and suggest promising areas for new research. We also suggest common reporting standards as a means of increasing the leverage of this powerful tool.\n\nThe environmental science, ecology and biodiversity categories of the Web of Knowledge were searched using the terms ‘Value of Information,’ ‘Expected Value of Perfect Information,’ and the abbreviation ‘EVPI.’ Google Scholar was searched with the same terms, and additionally the terms decision and biology, biodiversity conservation, fish, or ecology. We identified 1225 papers from these searches. Included studies were limited to those that show an application of VoI in biodiversity conservation rather than simply describing the method. All examples of use of VOI were summarised regarding the application of VoI, the management objectives, the uncertainties, models used, how the objectives were measured, and the type of VoI.\n\nWhile the use of VoI appears to be on the increase in biodiversity conservation, the reporting of results is highly variable, which can make it difficult to understand the decision context and which uncertainties were considered. Moreover, it was unclear if, and how, the papers informed management and policy interventions, which is why we suggest a range of reporting standards that would aid the use of VoI.\n\nThe use of VoI in conservation settings is at an early stage. There are opportunities for broader applications, not only for species-focussed management problems, but also for setting local or global research priorities for biodiversity conservation, making funding decisions, or designing or improving protected area networks and management. The long-term benefits of applying VoI methods to biodiversity conservation include a more structured and decision-focused allocation of resources to research.","language":"English","publisher":"Wiley","doi":"10.1111/brv.12471","usgsCitation":"Bolam, F.C., Grainger, M.J., Mengerson, K.L., Stewart, G.B., Sutherland, W.J., Runge, M.C., and McGowan, P., 2019, Using the value of information to improve conservation decision making: Biological Reviews, v. 94, no. 2, p. 629-647, https://doi.org/10.1111/brv.12471.","productDescription":"19 p.","startPage":"629","endPage":"647","ipdsId":"IP-092321","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":467744,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.repository.cam.ac.uk/handle/1810/286798","text":"External Repository"},{"id":362842,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"94","issue":"2","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationDate":"2018-10-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Bolam, Friederike C.","contributorId":214679,"corporation":false,"usgs":false,"family":"Bolam","given":"Friederike","email":"","middleInitial":"C.","affiliations":[{"id":33636,"text":"Newcastle University","active":true,"usgs":false}],"preferred":false,"id":760545,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grainger, Matthew J.","contributorId":214680,"corporation":false,"usgs":false,"family":"Grainger","given":"Matthew","email":"","middleInitial":"J.","affiliations":[{"id":33636,"text":"Newcastle University","active":true,"usgs":false}],"preferred":false,"id":760546,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mengerson, Kerrie L.","contributorId":214681,"corporation":false,"usgs":false,"family":"Mengerson","given":"Kerrie","email":"","middleInitial":"L.","affiliations":[{"id":37600,"text":"Queensland University of Technology","active":true,"usgs":false}],"preferred":false,"id":760547,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stewart, Gavin B.","contributorId":214682,"corporation":false,"usgs":false,"family":"Stewart","given":"Gavin","email":"","middleInitial":"B.","affiliations":[{"id":33636,"text":"Newcastle University","active":true,"usgs":false}],"preferred":false,"id":760548,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sutherland, William J.","contributorId":204319,"corporation":false,"usgs":false,"family":"Sutherland","given":"William","email":"","middleInitial":"J.","affiliations":[{"id":36918,"text":"Conservation Science Group, Department of Zoology, University of Cambridge, Cambridge CB2 3QZ, UK","active":true,"usgs":false}],"preferred":false,"id":760549,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":760544,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"McGowan, Philip J. K.","contributorId":214683,"corporation":false,"usgs":false,"family":"McGowan","given":"Philip J. K.","affiliations":[{"id":33636,"text":"Newcastle University","active":true,"usgs":false}],"preferred":false,"id":760550,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70203428,"text":"70203428 - 2019 - Offshore landslide hazard curves from mapped landslide size distributions","interactions":[],"lastModifiedDate":"2019-06-18T12:05:49","indexId":"70203428","displayToPublicDate":"2019-04-01T12:06:16","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2314,"text":"Journal of Geophysical Research B: Solid Earth","active":true,"publicationSubtype":{"id":10}},"title":"Offshore landslide hazard curves from mapped landslide size distributions","docAbstract":"We present a method to calculate landslide hazard curves along offshore margins based on size distributions of submarine landslides. The method analyzes ten different continental margins, that were mapped by high-resolution multibeam sonar with landslide scar areas measured by a consistent GIS procedure. Statistical tests of several different probability distribution models indicate that the lognormal model is most appropriate for these siliciclastic environments, consistent with an earlier study of the U.S. Atlantic margin [Chaytor et al., 2009]. Parameter estimation is performed using the maximum likelihood technique and confidence intervals are determined using likelihood profiles. Pairwise comparison of size distributions for the ten margins indicates that the U.S. Atlantic and Queen Charlotte margins are different than most other margins. These margins represent end members, with the U.S. Atlantic margin having the highest mean scar area and the Queen Charlotte margin, the lowest. We demonstrate that empirical, offshore landslide hazard curves can be developed from the landslide size distributions, if the duration of mapped landslide activity is known. This study indicates that the shape parameter of the size distribution is similar among all ten margins and thus the shape of the hazard curves is also similar. Significant differences in hazard curves among the margins are therefore related to differences in mean sizes and, potentially, differences in the duration of landslide activity.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2018JB017236","usgsCitation":"Geist, E.L., and ten Brink, U., 2019, Offshore landslide hazard curves from mapped landslide size distributions: Journal of Geophysical Research B: Solid Earth, v. 124, no. 4, p. 3320-3334, https://doi.org/10.1029/2018JB017236.","productDescription":"15 p.","startPage":"3320","endPage":"3334","ipdsId":"IP-103389","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":460421,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/1912/24319","text":"External Repository"},{"id":363766,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"124","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-04-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Geist, Eric L. 0000-0003-0611-1150 egeist@usgs.gov","orcid":"https://orcid.org/0000-0003-0611-1150","contributorId":1956,"corporation":false,"usgs":true,"family":"Geist","given":"Eric","email":"egeist@usgs.gov","middleInitial":"L.","affiliations":[{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":762670,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"ten Brink, Uri S. 0000-0001-6858-3001 utenbrink@usgs.gov","orcid":"https://orcid.org/0000-0001-6858-3001","contributorId":127560,"corporation":false,"usgs":true,"family":"ten Brink","given":"Uri S.","email":"utenbrink@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":false,"id":762671,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70202797,"text":"ofr20191031 - 2019 - Life-history model for sockeye salmon (Oncorhynchus nerka) at Lake Ozette, northwestern Washington—Users' guide","interactions":[],"lastModifiedDate":"2019-04-05T14:49:39","indexId":"ofr20191031","displayToPublicDate":"2019-04-01T12:03:46","publicationYear":"2019","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":"2019-1031","displayTitle":"Life-History Model for Sockeye Salmon (Oncorhynchus nerka) at Lake Ozette, Northwestern Washington—Users’ Guide","title":"Life-history model for sockeye salmon (Oncorhynchus nerka) at Lake Ozette, northwestern Washington—Users' guide","docAbstract":"Salmon populations spawning in the Lake Ozette watershed of northwestern Washington were once sufficiently abundant to support traditional Tribal fisheries, and were later harvested by settlers. However, in 1974 and 1975, the sockeye salmon (Oncorhynchus nerka) harvest decreased to 0 from a high of more than 17,500 in 1949, thus stimulating research into the causes of decrease, which resulted in eventual listing of the population as threatened under the Endangered Species Act in 1999. The listing status was upheld in 2005 and 2014 following 5-year reviews. Meanwhile, research results were compiled in a limiting factors analysis (LFA) and a recovery plan was developed. Although there has been some improvement in sockeye abundance since listing, the numbers remain too low to allow harvest and it is not yet clear which of the many potential limiting factors are most consequential.
As part of the LFA process, a population model was developed to determine values of life-history parameters that would enable the population to survive for 100 years. The model was based on the best available data, but data are limited for the Lake Ozette system. Results informed the qualitative assessment of the importance of limiting factors used to develop the recovery plan for Lake Ozette sockeye. The model was built in Microsoft Excel® and is difficult to use. The purpose of the model described herein is to synthesize the results of the LFA in a form that can be manipulated by resource managers and the public to create scenarios, test hypotheses, and observe sensitivities of results to changes in parameters. The goal is to provide a tool that enables research, monitoring and management to be focused on the most impactful elements and processes, including identifying the information gaps that are most critical to fill.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191031","collaboration":"Prepared in cooperation with the National Park Service","usgsCitation":"Woodward, A., Haggerty, M., and Crain, P., 2019, Life-history model for sockeye salmon (Oncorhynchus nerka) at Lake Ozette, northwestern Washington—Users' guide: U.S. Geological Survey Open-File Report 2019-1031, 79 p., https://doi.org/10.3133/ofr20191031.","productDescription":"viii, 79 p.","numberOfPages":"92","onlineOnly":"Y","ipdsId":"IP-101934","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":362633,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1031/coverthb.jpg"},{"id":362634,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1031/ofr20191031.pdf","text":"Report","size":"4.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1031"}],"country":"United States","state":"Washington","otherGeospatial":"Lake Ozette","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.68074798583986,\n 48.033560004128255\n ],\n [\n -124.59320068359374,\n 48.033560004128255\n ],\n [\n -124.59320068359374,\n 48.15509285476017\n ],\n [\n -124.68074798583986,\n 48.15509285476017\n ],\n [\n -124.68074798583986,\n 48.033560004128255\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Forest and Rangeland Ecosystem Science Center
U.S. Geological Survey
777 NW 9th St., Suite 400
Corvallis, Oregon 97330
Occupancy models are widely applied to estimate species distributions, but few methods exist for model checking. Thorough model assessments can uncover inadequacies and allow for deeper ecological insight by exploring structure in the observed data not accounted for by a model. We introduce occupancy model residual definitions that utilize the posterior distribution of the partially latent occupancy states. Residual‐based assessments are valuable because they can target specific assumptions and identify ways to improve a model, such as adding spatial correlation or meaningful covariates. Our approach defines separate residuals for occupancy and detection, and we use simulation to examine whether missing structure for modeling detection probabilities can be distinguished from that for occupancy probabilities. In many scenarios, our residual diagnostics were able to successfully separate inadequacies at the different model levels, but we describe other situations when this may not be the case. Applying Moran's I residual diagnostics to assess models for silver‐haired (Lasionycteris noctivagans) and little brown (Myotis lucifugus) bats only provided evidence of residual spatial correlation among detections. Targeting specific model assumptions using carefully chosen residual diagnostics is valuable for any analysis, and we remove previous barriers for occupancy analyses — lack of examples and practical advice.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecy.2703","usgsCitation":"Wright, W., Irvine, K., and Higgs, M.D., 2019, Identifying occupancy model inadequacies: Can residuals separately assess detection and presence?: Ecology, v. 100, no. 6, e02703, https://doi.org/10.1002/ecy.2703.","productDescription":"e02703","ipdsId":"IP-088414","costCenters":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":467749,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/ecy.2703","text":"External Repository"},{"id":362650,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"100","issue":"6","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2019-04-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Wright, Wilson 0000-0003-4276-3850","orcid":"https://orcid.org/0000-0003-4276-3850","contributorId":214592,"corporation":false,"usgs":true,"family":"Wright","given":"Wilson","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":760332,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Irvine, Kathryn M. 0000-0002-6426-940X","orcid":"https://orcid.org/0000-0002-6426-940X","contributorId":214591,"corporation":false,"usgs":true,"family":"Irvine","given":"Kathryn M.","affiliations":[{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"preferred":true,"id":760331,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Higgs, Megan D.","contributorId":127365,"corporation":false,"usgs":false,"family":"Higgs","given":"Megan","email":"","middleInitial":"D.","affiliations":[{"id":6916,"text":"Department of Mathematical Sciences, Montana State University, Bozeman, USA","active":true,"usgs":false}],"preferred":false,"id":760333,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70199964,"text":"70199964 - 2019 - Geospatial data mining for digital raster mapping","interactions":[],"lastModifiedDate":"2024-05-17T15:09:45.727773","indexId":"70199964","displayToPublicDate":"2019-04-01T10:38:33","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1722,"text":"GIScience and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Geospatial data mining for digital raster mapping","docAbstract":"We performed an in-depth literature survey to identify the most popular data mining approaches that have been applied for raster mapping of ecological parameters through the use of Geographic Information Systems (GIS) and remotely sensed data. Popular data mining approaches included decision trees or “data mining” trees which consist of regression and classification trees, random forests, neural networks, and support vector machines. The advantages of each data mining approach as well as approaches to avoid overfitting are subsequently discussed. We also provide suggestions and examples for the mapping of problematic variables or classes, future or historical projections, and avoidance of model bias. Finally, we address the separate issues of parallel processing, error mapping, and incorporation of “no data” values into modeling processes. Given the improved availability of digital spatial products and remote sensing products, data mining approaches combined with parallel processing potentials should greatly improve the quality and extent of ecological datasets.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/15481603.2018.1517445","usgsCitation":"Wylie, B.K., Pastick, N.J., Picotte, J.J., and Deering, C., 2019, Geospatial data mining for digital raster mapping: GIScience and Remote Sensing, v. 56, no. 3, p. 406-429, https://doi.org/10.1080/15481603.2018.1517445.","productDescription":"14 p.","startPage":"406","endPage":"429","ipdsId":"IP-094736","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":499974,"rank":2,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/7c16e86b33fd456cb54a7bd63a3e2985","text":"External Repository"},{"id":358204,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"56","issue":"3","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5bc02f76e4b0fc368eb53837","contributors":{"authors":[{"text":"Wylie, Bruce K. 0000-0002-7374-1083 wylie@usgs.gov","orcid":"https://orcid.org/0000-0002-7374-1083","contributorId":750,"corporation":false,"usgs":true,"family":"Wylie","given":"Bruce","email":"wylie@usgs.gov","middleInitial":"K.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":747499,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pastick, Neal J. 0000-0002-8169-3018 njpastick@usgs.gov","orcid":"https://orcid.org/0000-0002-8169-3018","contributorId":4785,"corporation":false,"usgs":true,"family":"Pastick","given":"Neal","email":"njpastick@usgs.gov","middleInitial":"J.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":747500,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Picotte, Joshua J. 0000-0002-4021-4623 jpicotte@usgs.gov","orcid":"https://orcid.org/0000-0002-4021-4623","contributorId":4626,"corporation":false,"usgs":true,"family":"Picotte","given":"Joshua","email":"jpicotte@usgs.gov","middleInitial":"J.","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":747501,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Deering, Carol 0000-0003-3565-6264 cdeering@usgs.gov","orcid":"https://orcid.org/0000-0003-3565-6264","contributorId":3001,"corporation":false,"usgs":true,"family":"Deering","given":"Carol","email":"cdeering@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":747502,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70203004,"text":"70203004 - 2019 - Diverse late‐stage crystallization and storage conditions in melt domains from the Youngest Toba Tuff revealed by age and compositional heterogeneity in the last increment of accessory phase growth","interactions":[],"lastModifiedDate":"2019-08-15T11:55:37","indexId":"70203004","displayToPublicDate":"2019-04-01T10:18:21","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1336,"text":"Contributions to Mineralogy and Petrology","active":true,"publicationSubtype":{"id":10}},"title":"Diverse late‐stage crystallization and storage conditions in melt domains from the Youngest Toba Tuff revealed by age and compositional heterogeneity in the last increment of accessory phase growth","docAbstract":"The chemical record contained within the nal increment of growth on crystals is utilized to reveal the dynamics and time- scales of magma assembly and storage before eruption of the cataclysmic 2800 km3 Youngest Toba Tu (YTT), Indonesia. In situ U–Th disequilibrium dates and trace element concentrations were obtained via secondary ionization mass spectrometry (SIMS) on unsectioned and unpolished faces of individual zircon and allanite crystals. The six high-silica (> 73 wt% SiO2) pumices from which crystals were derived are among the more evolved and lower crystallinity (< 25 wt%) pumices from the YTT eruption, and likely represent the melt-dominated portion of the magma system. Discrete SIMS measurement cycles were coupled with statistical treatments to detect zircon and allanite surface zoning domains at the ~ 1 μm scale. Coupled r-MELTS and accessory phase saturation modeling indicates that at the granite ternary minimum or ‘eutectoid’ conditions that de ne this portion of the YTT, zircon and allanite crystallization is dependent on and proportionate to major phase crystallization, and is more limited than at pre-eutectoid conditions. A lower proportion of near-eruption zircon surface ages in the comparatively cool and wet YTT relative to other hotter and drier voluminous silicic eruptions could re ect the in u- ence of eutectoid storage conditions on magmatic responses to remobilization-related magmatic recharge.","language":"English","publisher":"Springer","doi":"10.1007/s00410-019-1566-6","usgsCitation":"Tierney, C.R., Reid, M.R., Vazquez, J.A., and Chesner, C.A., 2019, Diverse late‐stage crystallization and storage conditions in melt domains from the Youngest Toba Tuff revealed by age and compositional heterogeneity in the last increment of accessory phase growth: Contributions to Mineralogy and Petrology, v. 174, 31, 21 p., https://doi.org/10.1007/s00410-019-1566-6.","productDescription":"31, 21 p.","ipdsId":"IP-106418","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":362910,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Indonesia","otherGeospatial":"Youngest Toba Tuff","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 98.41827392578125,\n 2.2324061399778894\n ],\n [\n 99.30816650390625,\n 2.2324061399778894\n ],\n [\n 99.30816650390625,\n 2.981441678317486\n ],\n [\n 98.41827392578125,\n 2.981441678317486\n ],\n [\n 98.41827392578125,\n 2.2324061399778894\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"174","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-04-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Tierney, Casey R.","contributorId":214772,"corporation":false,"usgs":false,"family":"Tierney","given":"Casey","email":"","middleInitial":"R.","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":760745,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reid, Mary R.","contributorId":192856,"corporation":false,"usgs":false,"family":"Reid","given":"Mary","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":760746,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Vazquez, Jorge A. 0000-0003-2754-0456 jvazquez@usgs.gov","orcid":"https://orcid.org/0000-0003-2754-0456","contributorId":4458,"corporation":false,"usgs":true,"family":"Vazquez","given":"Jorge","email":"jvazquez@usgs.gov","middleInitial":"A.","affiliations":[{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":5056,"text":"Office of the AD Energy and Minerals, and Environmental Health","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true}],"preferred":true,"id":760744,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chesner, Craig A.","contributorId":214773,"corporation":false,"usgs":false,"family":"Chesner","given":"Craig","email":"","middleInitial":"A.","affiliations":[{"id":5043,"text":"Eastern Illinois University","active":true,"usgs":false}],"preferred":false,"id":760747,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70203198,"text":"70203198 - 2019 - Development of a quantitative PCR method for screening ichthyoplankton samples for bigheaded carps","interactions":[],"lastModifiedDate":"2019-04-29T08:57:06","indexId":"70203198","displayToPublicDate":"2019-04-01T08:56:55","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1018,"text":"Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Development of a quantitative PCR method for screening ichthyoplankton samples for bigheaded carps","docAbstract":"Monitoring ichthyoplankton is useful for identifying reproductive fronts and spawning locations of bigheaded carps (Hypophthalmichthys spp.). Unfortunately, sorting and identifying ichthyoplankton to monitor for bigheaded carp reproduction is time consuming and expensive. Traditional methods require frequent egg-larvae sampling, sorting of all samples to obtain presumptively identified bigheaded carp, and genetic validation of presumptively identified eggs. Quantitative PCR (qPCR) has the potential to streamline this process by identifying samples that likely do or do not contain a target species. Our objective was to develop a genetic screening tool using qPCR with the duplex assays SCTM4/5 and BHTM1/2 to prioritize samples that have a higher likelihood of containing bigheaded carp eggs or larvae. We used tandem ichthyoplankton samples collected for monitoring bigheaded carps in the Upper Mississippi, Illinois, and St. Croix rivers to evaluate the effectiveness of qPCR as a screening tool. Samples with > 10,000 copies of DNA had 100% occurrence of bigheaded carp eggs or larvae in the traditionally sorted samples, whereas samples with < 10 copies of DNA had 0% occurrence of ichthyoplankton from these invasive species. We used a logistic regression model to calculate the probability of finding bigheaded carp eggs or larvae based upon the number of DNA copies; 406 copies corresponded with a 50% probability of having bigheaded carp ichthyoplankton present in a sample. These data can be used to inform management actions (i.e., control, containment) for these invasive fishes, and this tool could be adapted for monitoring for reproduction of other aquatic invasive species.","language":"English","publisher":"Springer","doi":"10.1007/s10530-018-1887-9","usgsCitation":"Fritts, A.K., Knights, B.C., Larson, J.H., Amberg, J., Merkes, C.M., Tajjioui, T., Butler, S.E., Diana, M.J., Wahl, D.H., Weber, M.J., and Waters, J.D., 2019, Development of a quantitative PCR method for screening ichthyoplankton samples for bigheaded carps: Biological Invasions, v. 21, no. 4, p. 1143-1153, https://doi.org/10.1007/s10530-018-1887-9.","productDescription":"11 p.","startPage":"1143","endPage":"1153","ipdsId":"IP-100744","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":467750,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10530-018-1887-9","text":"Publisher Index Page"},{"id":437518,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96BTBUH","text":"USGS data release","linkHelpText":"Bigheaded carp ichthyoplankton qPCR screening tool: data"},{"id":363288,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Illinois, Iowa, Minnesota, Missouri, Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.3388671875,\n 35.92464453144099\n ],\n [\n -87.0556640625,\n 35.92464453144099\n ],\n [\n -87.0556640625,\n 49.32512199104001\n ],\n [\n -97.3388671875,\n 49.32512199104001\n ],\n [\n -97.3388671875,\n 35.92464453144099\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"21","issue":"4","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Fritts, Andrea K. 0000-0003-2142-3339","orcid":"https://orcid.org/0000-0003-2142-3339","contributorId":204594,"corporation":false,"usgs":true,"family":"Fritts","given":"Andrea","email":"","middleInitial":"K.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":761601,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Knights, Brent C. 0000-0001-8526-8468 bknights@usgs.gov","orcid":"https://orcid.org/0000-0001-8526-8468","contributorId":2906,"corporation":false,"usgs":true,"family":"Knights","given":"Brent","email":"bknights@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":761602,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Larson, James H. 0000-0002-6414-9758 jhlarson@usgs.gov","orcid":"https://orcid.org/0000-0002-6414-9758","contributorId":4250,"corporation":false,"usgs":true,"family":"Larson","given":"James","email":"jhlarson@usgs.gov","middleInitial":"H.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":761603,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Amberg, Jon 0000-0002-8351-4861 jamberg@usgs.gov","orcid":"https://orcid.org/0000-0002-8351-4861","contributorId":149785,"corporation":false,"usgs":true,"family":"Amberg","given":"Jon","email":"jamberg@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":761604,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Merkes, Christopher M. 0000-0001-8191-627X cmerkes@usgs.gov","orcid":"https://orcid.org/0000-0001-8191-627X","contributorId":139516,"corporation":false,"usgs":true,"family":"Merkes","given":"Christopher","email":"cmerkes@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":761605,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tajjioui, Tariq 0000-0002-0113-0451","orcid":"https://orcid.org/0000-0002-0113-0451","contributorId":215091,"corporation":false,"usgs":true,"family":"Tajjioui","given":"Tariq","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":761606,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Butler, Steven E.","contributorId":206527,"corporation":false,"usgs":false,"family":"Butler","given":"Steven","email":"","middleInitial":"E.","affiliations":[{"id":37336,"text":"Illinois Natural History Survey, Kaskaskia Biological Station","active":true,"usgs":false}],"preferred":false,"id":761607,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Diana, Matthew J.","contributorId":206528,"corporation":false,"usgs":false,"family":"Diana","given":"Matthew","email":"","middleInitial":"J.","affiliations":[{"id":36986,"text":"Michigan Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":761608,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Wahl, David H.","contributorId":206529,"corporation":false,"usgs":false,"family":"Wahl","given":"David","email":"","middleInitial":"H.","affiliations":[{"id":37336,"text":"Illinois Natural History Survey, Kaskaskia Biological Station","active":true,"usgs":false}],"preferred":false,"id":761609,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Weber, Michael J. 0000-0003-0430-3087","orcid":"https://orcid.org/0000-0003-0430-3087","contributorId":210835,"corporation":false,"usgs":false,"family":"Weber","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":6911,"text":"Iowa State University","active":true,"usgs":false}],"preferred":false,"id":761610,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Waters, John D.","contributorId":215092,"corporation":false,"usgs":false,"family":"Waters","given":"John","email":"","middleInitial":"D.","affiliations":[{"id":6964,"text":"Minnesota Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":761611,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70210142,"text":"70210142 - 2019 - Increased nesting success of Hawaii Elepaio in response to the removal of invasive black rats","interactions":[],"lastModifiedDate":"2020-05-15T13:45:50.949288","indexId":"70210142","displayToPublicDate":"2019-04-01T08:39:01","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1318,"text":"Condor","active":true,"publicationSubtype":{"id":10}},"title":"Increased nesting success of Hawaii Elepaio in response to the removal of invasive black rats","docAbstract":"In Hawaii and other oceanic islands with few native land mammals, black rats (Rattus rattus) are among the most damaging invasive vertebrate species to native forest bird populations and habitats, due to their arboreal behavior and generalist foraging habits and habitat use. We evaluated the nesting response of Hawaii Elepaio (Chasiempis sandwichensis; Monarchidae), a generalist insectivore, to the removal of black rats using rodenticide in a before-after-control-impact study in high- and low-elevation mesic montane habitat recovering from long-term damage from introduced ungulates and weeds. We monitored nesting success and rat abundance during 2015–2016 before applying rodenticide bait in 2017 to remove rats from two 700 × 700 m treatment plots that were paired with 2 nontreatment plots of the same size. Rat abundance was reduced by 90% during treatment, with combined variables treatment and elevation best explaining the change using GLM methods and AIC model selection. The daily survival rate (DSR) of nests (n = 191) was greater on treated plots after rodenticide application (mean ± SE = 0.980 ± 0.004 treatment; 0.964 ± 0.004 nontreatment), modeled nest success increased from 29% to 50%, and apparent nest success (number of successful nests per total nests) increased from 37% to 52%. The most informative model for predicting DSR included the effect of treatment. Predation by rats was documented at 3 of 16 nests using video surveillance, and we observed additional evidence of rat predation during in-person nest monitoring. Rats targeted adults on the nest and sometimes removed intact eggs, leaving little trace of their activity. Our results demonstrate that reducing rat predation can immediately improve the nesting success of even a common bird species in habitat with a long history of forest restoration. Sustained predator control may be critical to accelerating the recovery of native forest bird communities.","language":"English","publisher":"Oxford Academic","doi":"10.1093/condor/duz003","collaboration":"","usgsCitation":"Banko, P.C., Jaenecke, K., Peck, R., and Brinck, K.W., 2019, Increased nesting success of Hawaii Elepaio in response to the removal of invasive black rats: Condor, v. 121, no. 2, duz003, 12 p., https://doi.org/10.1093/condor/duz003.","productDescription":"duz003, 12 p.","ipdsId":"IP-080105","costCenters":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":467752,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/condor/duz003","text":"Publisher Index Page"},{"id":437519,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93TOM58","text":"USGS data release","linkHelpText":"Hawaii Volcanoes National Park Elepaio nest monitoring and black rat mark recapture data 2015-2017"},{"id":374869,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -156.46728515625,\n 18.87510275035649\n ],\n [\n -154.75341796875,\n 18.87510275035649\n ],\n [\n -154.75341796875,\n 20.365227537412434\n ],\n [\n -156.46728515625,\n 20.365227537412434\n ],\n [\n -156.46728515625,\n 18.87510275035649\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"121","issue":"2","noUsgsAuthors":false,"publicationDate":"2019-04-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Banko, Paul C. 0000-0002-6035-9803 pbanko@usgs.gov","orcid":"https://orcid.org/0000-0002-6035-9803","contributorId":3179,"corporation":false,"usgs":true,"family":"Banko","given":"Paul","email":"pbanko@usgs.gov","middleInitial":"C.","affiliations":[{"id":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":789282,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jaenecke, Kelly 0000-0002-7124-4788","orcid":"https://orcid.org/0000-0002-7124-4788","contributorId":211063,"corporation":false,"usgs":false,"family":"Jaenecke","given":"Kelly","email":"","affiliations":[{"id":13341,"text":"Hawai‘i Cooperative Studies Unit, University of Hawai‘i at Hilo","active":true,"usgs":false}],"preferred":false,"id":789283,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peck, Robert W. 0000-0002-8739-9493","orcid":"https://orcid.org/0000-0002-8739-9493","contributorId":193088,"corporation":false,"usgs":false,"family":"Peck","given":"Robert W.","affiliations":[],"preferred":false,"id":789284,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brinck, Kevin W. 0000-0001-7581-2482 kbrinck@usgs.gov","orcid":"https://orcid.org/0000-0001-7581-2482","contributorId":150936,"corporation":false,"usgs":false,"family":"Brinck","given":"Kevin","email":"kbrinck@usgs.gov","middleInitial":"W.","affiliations":[{"id":13351,"text":"University of Hawaii Cooperative Studies Unit","active":true,"usgs":false}],"preferred":false,"id":789285,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70203203,"text":"70203203 - 2019 - Consequences of ignoring spatial variation in population trend when conducting a power analysis","interactions":[],"lastModifiedDate":"2019-04-29T08:39:06","indexId":"70203203","displayToPublicDate":"2019-04-01T08:38:43","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1445,"text":"Ecography","active":true,"publicationSubtype":{"id":10}},"title":"Consequences of ignoring spatial variation in population trend when conducting a power analysis","docAbstract":"Long-term, large-scale monitoring programs are becoming increasingly common to document status and trends of wild populations. A successful program for monitoring population trend hinges on the ability to detect the trend of interest. Power analyses are useful for quantifying the sample size needed for trend detection, given expected variation in the population. Four components of variation (within-year variation at a given site, interannual variation within a site, variation among sites in the interannual variation, and variation among sites in mean abundance or density) are commonly considered in power analyses for population trend, but a fifth is not: variation among sites in the local trend. Spatial variation in trend is expected to reduce statistical power, but the magnitude of this reduction has not been fully explored. We used computer simulations to evaluate the consequences of ignoring spatial variation in trend under a variety of sampling designs and wide ranges of other components of variation. The effect of spatial variation in trend on power was minor when other input parameters took extreme values that made the trend either very difficult or very easy to detect. However, at moderate values of the other parameters, spatial variation in trend had a strong effect, reducing statistical power by up to 60%. In some cases, ignoring spatial variation in trend resulted in an 80% probability of a Type I error (falsely detecting a trend in a stable population). Spatial variation in trend is therefore an important consideration when designing a long-term monitoring program for many species, especially those affected by local conditions at sites that are repeatedly surveyed. If variation in trend is ignored, as in most previous power analyses, the recommended sampling design will likely be insufficient to detect the trend of interest and lead to potentially false conclusions of a stable population.","language":"English","publisher":"Wiley-Blackwell","doi":"10.1111/ecog.04093","usgsCitation":"Weiser, E.L., Diffendorfer, J., Lopez-Hoffman, L., Semmens, D.J., and Thogmartin, W.E., 2019, Consequences of ignoring spatial variation in population trend when conducting a power analysis: Ecography, v. 42, no. 4, p. 836-844, https://doi.org/10.1111/ecog.04093.","productDescription":"9 p.","startPage":"836","endPage":"844","ipdsId":"IP-091066","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":437520,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SFUH2K","text":"USGS data release","linkHelpText":"Power analysis code"},{"id":363285,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"42","issue":"4","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationDate":"2018-12-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Weiser, Emily L. 0000-0003-1598-659X","orcid":"https://orcid.org/0000-0003-1598-659X","contributorId":213770,"corporation":false,"usgs":true,"family":"Weiser","given":"Emily","email":"","middleInitial":"L.","affiliations":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"preferred":true,"id":761631,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Diffendorfer, James E. 0000-0003-1093-6948 jediffendorfer@usgs.gov","orcid":"https://orcid.org/0000-0003-1093-6948","contributorId":3208,"corporation":false,"usgs":true,"family":"Diffendorfer","given":"James E.","email":"jediffendorfer@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true},{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":761632,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lopez-Hoffman, Laura","contributorId":149127,"corporation":false,"usgs":false,"family":"Lopez-Hoffman","given":"Laura","affiliations":[{"id":17654,"text":"School of Natural Resources & the Environment and Udall Center for Studies in Public Policy, The University of Arizona, Tucson","active":true,"usgs":false}],"preferred":false,"id":761633,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Semmens, Darius J. 0000-0001-7924-6529 dsemmens@usgs.gov","orcid":"https://orcid.org/0000-0001-7924-6529","contributorId":1714,"corporation":false,"usgs":true,"family":"Semmens","given":"Darius","email":"dsemmens@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":761634,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":761635,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70203136,"text":"70203136 - 2019 - AVO-G2S: A modified, open-source Ground-to-Space atmospheric specification for infrasound modeling","interactions":[],"lastModifiedDate":"2019-04-24T08:16:39","indexId":"70203136","displayToPublicDate":"2019-04-01T08:16:10","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1315,"text":"Computers & Geosciences","printIssn":"0098-3004","active":true,"publicationSubtype":{"id":10}},"title":"AVO-G2S: A modified, open-source Ground-to-Space atmospheric specification for infrasound modeling","docAbstract":"To facilitate infrasound propagation studies, we present AVO-G2S, an open-source, Ground-to-Space model which provides temperature and wind specifications from the surface to an altitude of 225 km. This model provides a means of smoothly characterizing atmospheric conditions using multiple numerical weather prediction forecast and reanalysis products, along with upper-atmospheric empirical models. Regional atmospheric reconstructions only require a limited domain and can utilize high-resolution numerical weather prediction forecasts typically provided\non a projected grid. The use of a projected grid allows for faster spectral transform libraries to be\nemployed. The AVO-G2S software can also provide global reconstructions that rely on global\nnumerical weather prediction products and spherical harmonic decompositions. AVO-G2S is inspired by a global Ground-to-Space model developed by the Naval Research Laboratory, and relies on their empirical descriptions of upper-atmospheric conditions. Alaska Volcano Observatory has implemented this model for near-real-time infrasound monitoring of volcanic eruptions and historical research projects.","language":"English","publisher":"Elsevier","doi":"10.1016/j.cageo.2018.12.013","usgsCitation":"Schwaiger, H., Iezzi, A., and Fee, D., 2019, AVO-G2S: A modified, open-source Ground-to-Space atmospheric specification for infrasound modeling: Computers & Geosciences, v. 125, p. 90-97, https://doi.org/10.1016/j.cageo.2018.12.013.","productDescription":"8 p.","startPage":"90","endPage":"97","ipdsId":"IP-091624","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467753,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.cageo.2018.12.013","text":"Publisher Index Page"},{"id":363165,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"125","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Schwaiger, Hans 0000-0001-7397-8833","orcid":"https://orcid.org/0000-0001-7397-8833","contributorId":214983,"corporation":false,"usgs":true,"family":"Schwaiger","given":"Hans","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":761353,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Iezzi, Alexandra M. 0000-0002-6782-7681","orcid":"https://orcid.org/0000-0002-6782-7681","contributorId":196436,"corporation":false,"usgs":false,"family":"Iezzi","given":"Alexandra M.","affiliations":[],"preferred":false,"id":761354,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fee, David","contributorId":199660,"corporation":false,"usgs":false,"family":"Fee","given":"David","affiliations":[],"preferred":false,"id":761355,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70203230,"text":"70203230 - 2019 - Simulating the effects of climate variability on waterbodies and wetland-dependent birds in the Prairie Pothole Region","interactions":[],"lastModifiedDate":"2019-05-02T08:07:59","indexId":"70203230","displayToPublicDate":"2019-04-01T07:46:05","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Simulating the effects of climate variability on waterbodies and wetland-dependent birds in the Prairie Pothole Region","docAbstract":"Understanding how bird populations respond to changes in waterbody availability in the climatically variable Prairie Pothole Region (PPR) of North America hinges on being able to couple hydrological and climate modeling to represent potential future landscapes. Model experiments run with the Pothole Complex Hydrologic Model using downscaled climate data (variables relating to precipitation, temperature, and potential evapotranspiration at 1/8° spatial resolution under four general circulation climate models and two gas emissions scenarios) were used to forecast the abundances of six focal wetland‐dependent bird species in the Missouri Coteau portion of the PPR, providing ensemble scenarios at a spatial scale relevant to resource management. Although the projected number of May ponds (waterbodies present during bird breeding season) fluctuated through time with some decadal periodicity (and with the number present in a given year reflecting abundance over the previous three years), the ensemble model average indicated an increase in the average number of waterbodies present by the turn of the next century. Overall, the model experiments conservatively projected an 11.75% increase in the number of waterbodies present by 2090–2099 compared to a baseline period from 1967 to 2005 in the PPR. Wetland‐dependent bird occurrence and abundance were significantly associated with temporal patterns and decadal periodicity in waterbody dynamics. Because of the strong associations between wetland‐dependent bird occurrence and abundance and the number of prairie potholes, projected waterbody increases are forecasted to result in an 11.97% overall increase in occurrence and 8.63% increase in abundance of the six focal species by the end of the 21st century; these results contrast with forecasted drought‐associated declines in waterbodies and birds in the PPR. This integrated hydrological–climatological approach offers a means of assessing how wetland‐dependent bird populations may respond to changes in wetland habitat availability due to a changing climate. Our results provide information that can help managers decide how to mitigate the effects of climate shifts on the distribution of wetland habitat and biota.
Beginning in the late 1970s, 10- to 15-year cyclical oscillations in salinity were observed at lower Colorado River monitoring sites, moving upstream from the international border with Mexico, above Imperial Dam, below Hoover Dam, and at Lees Ferry. The cause of these cyclical trends in salinity was unknown. These salinity cycles complicate the U.S. Bureau of Reclamation's (Reclamation) responsibility for managing salinity in the river for delivery of water to Mexico to meet treaty obligations. This study develops a conceptual model of the salinity cycles from time-series water quality, streamflow, and precipitation data in both the lower and upper Colorado River Basins in order to provide Reclamation the ability to understand, anticipate, and manage future salinity cycles in the lower river. Compared with the Lees Ferry record, both maximum and minimum salinity levels increase downstream by about 25% at Hoover Dam, by about 49% at Imperial Dam, and by about 69% at the northern international boundary with Mexico. In the upper basin, cyclical salinity trends are evident at the outflow of three major tributaries, where salinity is also noted to be inversely related to streamflow. Time series trends in precipitation within the catchments of the three upper basin tributaries indicate cyclical periods with above normal precipitation and periods with below normal precipitation. Periods of greater than normal precipitation in the contributing areas correspond with declines in salinity at the catchment monitoring sites and periods of less than normal precipitation correspond with rising salinity at the sites. Based on the conceptual model developed in this investigation, a multiple linear regression model was developed using a stepwise variable selection procedure to simulate salinity in Lake Powell inflow. Important variables in the explanation of salinity entering Lake Powell include flow from the three upper basin tributaries, seasonality, and mean precipitation in the upper basin, among others. The root mean square error of prediction for the MLR model was 31.48 mg/L (5.7%).