Billions of animals migrate to track seasonal pulses in resources. Optimally timing migration is a key strategy, yet the ability of animals to compensate for phenological mismatches en route is largely unknown. Using GPS movement data collected from 72 adult female deer over a 10-year duration, we study a population of mule deer (Odocoileus hemionus) in Wyoming that lack reliable cues on their desert winter range, causing them to start migration 70 days ahead to 52 days behind the wave of spring green-up. We show that individual deer arrive at their summer range within an average 6-day window by adjusting movement speed and stopover use. Late migrants move 2.5 times faster and spend 72% less time on stopovers than early migrants, which allows them to catch the green wave. Our findings suggest that ungulates, and potentially other migratory species, possess cognitive abilities to recognize where they are in space and time relative to key resources. Such behavioral capacity may allow migratory taxa to maintain foraging benefits amid rapidly changing phenology.
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\"}}]}","volume":"14","noUsgsAuthors":false,"publicationDate":"2023-04-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Ortega, Anna C.","contributorId":351885,"corporation":false,"usgs":false,"family":"Ortega","given":"Anna C.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":929708,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Aikens, Ellen O.","contributorId":287295,"corporation":false,"usgs":false,"family":"Aikens","given":"Ellen O.","affiliations":[{"id":561,"text":"South Dakota Cooperative Fish and Wildlife Research Unit","active":false,"usgs":true}],"preferred":false,"id":929891,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Merkle, Jerod A.","contributorId":351886,"corporation":false,"usgs":false,"family":"Merkle","given":"Jerod A.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":929710,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Monteith, Kevin L.","contributorId":351887,"corporation":false,"usgs":false,"family":"Monteith","given":"Kevin L.","affiliations":[{"id":36628,"text":"University of Wyoming","active":true,"usgs":false}],"preferred":false,"id":929711,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Kauffman, Matthew J. 0000-0003-0127-3900","orcid":"https://orcid.org/0000-0003-0127-3900","contributorId":202921,"corporation":false,"usgs":true,"family":"Kauffman","given":"Matthew","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":929712,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70245407,"text":"70245407 - 2023 - Evaluating and mitigating the impact of systematic geolocation error on canopy height measurement performance of GEDI","interactions":[],"lastModifiedDate":"2023-06-23T13:33:45.396836","indexId":"70245407","displayToPublicDate":"2023-04-08T08:30:43","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3254,"text":"Remote Sensing of Environment","printIssn":"0034-4257","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating and mitigating the impact of systematic geolocation error on canopy height measurement performance of GEDI","docAbstract":"NASA's Global Ecosystem Dynamics Investigation (GEDI) is designed to provide high-resolution measurements of forest structure and topography between 52° N and S. However, current geolocation accuracy may limit further science applications of footprint-level products as early adopters have found it difficult to align with in-situ forestry inventory data and high-resolution imagery for calibration and validation purpose. Here we developed a new means to rapidly evaluate and mitigate the impact of systematic geolocation error on the performance of GEDI's forest height estimates in the US. By integrating nationwide high-resolution airborne lidar data collected through the 3D Elevation Program of the USGS, we provided optimal geolocation adjustments of GEDI at per beam level and tracked their performances over the first 18-mo. Our results suggest that the first release of GEDI product (R01) can have large systematic geolocation errors at beam level (i.e., 50.5% of beams with an error > 20 m). Its impact on canopy height measurement could drastically vary in space and time, which in turn also offers a separate indirect method to evaluate and track geolocation performance. The second release of GEDI data (R02) has achieved a much-improved systematic geolocation accuracy which is shown to meet the mission requirement (0.2% beams >20 m and 80.8% beams <10 m) and should be able to meet requirements from many practical science applications tolerant to moderate geolocation errors. In sum, our approach has provided a short-term solution for an enhanced Cal/Val strategy for GEDI. While further improvements will certainly be made in future releases, it can potentially create an alternative pathway to generate and validate biomass products by linking GEDI footprint samples directly with in-situ data collections.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2023.113571","usgsCitation":"Tang, H., Stoker, J.M., Luthcke, S., Armston, J., Lee, K., Blair, B., and Hofton, M., 2023, Evaluating and mitigating the impact of systematic geolocation error on canopy height measurement performance of GEDI: Remote Sensing of Environment, v. 291, 113571, 13 p., https://doi.org/10.1016/j.rse.2023.113571.","productDescription":"113571, 13 p.","ipdsId":"IP-144120","costCenters":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"links":[{"id":443909,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2023.113571","text":"Publisher Index Page"},{"id":418400,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":418399,"rank":1,"type":{"id":12,"text":"Errata"},"url":"https://doi.org/10.1016/j.rse.2023.113663"}],"volume":"291","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Tang, Hao","contributorId":311206,"corporation":false,"usgs":false,"family":"Tang","given":"Hao","email":"","affiliations":[{"id":67355,"text":"Department of Geography, National University of Singapore, 117570, Singapore","active":true,"usgs":false}],"preferred":false,"id":876037,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Stoker, Jason M. 0000-0003-2455-0931 jstoker@usgs.gov","orcid":"https://orcid.org/0000-0003-2455-0931","contributorId":3021,"corporation":false,"usgs":true,"family":"Stoker","given":"Jason","email":"jstoker@usgs.gov","middleInitial":"M.","affiliations":[{"id":223,"text":"Earth Resources Observation and Science (EROS) Center (Geography)","active":false,"usgs":true},{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":876038,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Luthcke, Scott","contributorId":311207,"corporation":false,"usgs":false,"family":"Luthcke","given":"Scott","affiliations":[{"id":67357,"text":"NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA","active":true,"usgs":false}],"preferred":false,"id":876039,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Armston, John","contributorId":311208,"corporation":false,"usgs":false,"family":"Armston","given":"John","email":"","affiliations":[{"id":67358,"text":"Department of Geographical Sciences, University of Maryland, College Park, MD 20770, USA","active":true,"usgs":false}],"preferred":false,"id":876040,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lee, Kyungtae","contributorId":311209,"corporation":false,"usgs":false,"family":"Lee","given":"Kyungtae","email":"","affiliations":[{"id":67358,"text":"Department of Geographical Sciences, University of Maryland, College Park, MD 20770, USA","active":true,"usgs":false}],"preferred":false,"id":876042,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Blair, Bryan","contributorId":311210,"corporation":false,"usgs":false,"family":"Blair","given":"Bryan","email":"","affiliations":[{"id":67357,"text":"NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA","active":true,"usgs":false}],"preferred":false,"id":876043,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hofton, Michelle","contributorId":311211,"corporation":false,"usgs":false,"family":"Hofton","given":"Michelle","email":"","affiliations":[{"id":67358,"text":"Department of Geographical Sciences, University of Maryland, College Park, MD 20770, USA","active":true,"usgs":false}],"preferred":false,"id":876044,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70242103,"text":"dr1171 - 2023 - Distribution and abundance of Least Bell’s Vireos (Vireo bellii pusillus), Southwestern Willow Flycatchers (Empidonax traillii extimus), and Coastal California Gnatcatchers (Polioptila californica californica) at the Santa Fe Dam, Los Angeles County, California—2022 data summary","interactions":[],"lastModifiedDate":"2023-04-10T18:35:11.346909","indexId":"dr1171","displayToPublicDate":"2023-04-07T10:42:46","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":9318,"text":"Data Report","code":"DR","onlineIssn":"2771-9448","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1171","displayTitle":"Distribution and Abundance of Least Bell’s Vireos (Vireo bellii pusillus), Southwestern Willow Flycatchers (Empidonax traillii extimus), and Coastal California Gnatcatchers (Polioptila californica californica) at the Santa Fe Dam, Los Angeles County, California—2022 Data Summary","title":"Distribution and abundance of Least Bell’s Vireos (Vireo bellii pusillus), Southwestern Willow Flycatchers (Empidonax traillii extimus), and Coastal California Gnatcatchers (Polioptila californica californica) at the Santa Fe Dam, Los Angeles County, California—2022 data summary","docAbstract":"In 2022, we surveyed for Least Bell’s Vireos (Vireo bellii pusillus; vireo), Southwestern Willow Flycatchers (Empidonax traillii extimus; flycatcher), and Coastal California Gnatcatchers (Polioptila californica californica; gnatcatcher) in the Santa Fe Dam detention basin and along the San Gabriel River upstream from the Santa Fe Dam near Irwindale, California. Four vireo surveys were completed between April 21 and July 13, 2022; three flycatcher surveys were completed between May 18 and July 13, 2022; and four gnatcatcher surveys were completed between April 21 and July 13, 2022.
We detected seven territorial male vireos, including four that were paired and three with undetermined breeding status. We also detected one transient vireo. Two juvenile vireos were observed during surveys. Vireo territories were found in riparian scrub, willow (Salix spp.)-cottonwood (Populus spp.), and mixed willow habitat, with mixed willow the most commonly-recorded habitat type. Black willow (S. gooddingii) was the dominant plant species in most vireo territories.
We detected 10 transient flycatchers in riparian scrub (5 individuals), mixed willow (4 individuals), and non-native vegetation (1 individual). Black willow and mule fat (Baccharis salicifolia) were the predominant plant species in flycatcher locations.
We detected four territorial male gnatcatchers, two of which were paired and two of undetermined breeding status. We also detected one territorial female gnatcatcher. One juvenile gnatcatcher was observed during surveys. All gnatcatchers were detected in coastal sage scrub. The dominant shrub species at gnatcatcher locations were California sagebrush (Artemisia californica) and scale broom (Lepidospartum squamatum).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/dr1171","programNote":"Ecosystems Mission Area—Species Management Research Program","usgsCitation":"Lynn, S., and Kus, B.E., 2023, Distribution and abundance of Least Bell’s Vireos (Vireo bellii pusillus), Southwestern Willow Flycatchers (Empidonax traillii extimus), and Coastal California Gnatcatchers (Polioptila californica californica) at the Santa Fe Dam, Los Angeles County, California—2022 data summary: U.S. Geological Survey Data Report 1171, 12 p., https://doi.org/10.3133/dr1171.","productDescription":"vi, 12 p.","numberOfPages":"12","onlineOnly":"Y","ipdsId":"IP-147582","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":415357,"rank":5,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/dr1171/full"},{"id":415356,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/dr/1171/images"},{"id":415354,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/dr/1171/dr1171.pdf","text":"Report","size":"3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Data Report 1171"},{"id":415355,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/dr/1171/dr1171.xml"},{"id":415353,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/dr/1171/covrthb.jpg"}],"country":"United States","state":"California","county":"Los Angeles County","otherGeospatial":"Santa Fe Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.99541701449124,\n 34.09714643780791\n ],\n [\n -117.90583912500122,\n 34.09714643780791\n ],\n [\n -117.90583912500122,\n 34.16481614556767\n ],\n [\n -117.99541701449124,\n 34.16481614556767\n ],\n [\n -117.99541701449124,\n 34.09714643780791\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Global change has altered the nature of disturbance regimes, and megafire events are increasingly common. Megafires result in immediate changes to habitat available to terrestrial wildlife over broad landscapes, yet we know surprisingly little about how such changes shape space use of sensitive species in habitat that remains. Functional responses provide a framework for understanding and predicting changes in space use following habitat alteration, but no previous studies have assessed functional responses as a consequence of megafire. We studied space use and tested for functional responses in habitat use by breeding greater sage-grouse (Centrocercus urophasianus) before and after landscape-level changes induced by a >40,000 ha, high-intensity megafire that burned sagebrush steppe in eastern Idaho, USA. We also incorporated functional responses into predictive resource selection functions (RSFs) to map breeding habitat before and after the fire. Megafire had strong effects on the distribution of available resources and resulted in context-dependent habitat use that was heterogeneous across different components of habitat. We observed functional responses in the use and selection of a variety of resources (shrubs and herbaceous vegetation) for both nesting and brood rearing. Functional responses in the use of nesting habitat were influenced by the overarching effect of megafire on vegetation, whereas responses during brood rearing appeared to be driven by individual variation in available resources that were conditional on nest locations. Importantly, RSFs built using data collected prior to the burn also had poor transferability for predicting space use in a post-megafire landscape. These results have strong implications for understanding and predicting how animals respond to a rapidly changing environment, given that increased severity, frequency, and extent of wildfire are consequences of global change with the capacity to reshape ecosystems. We therefore demonstrate a conceptual framework to better understand space use and aid habitat conservation for wildlife in a rapidly changing world.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.9933","usgsCitation":"Stevens, B.S., Roberts, S., Conway, C.J., and Engelstead, D.K., 2023, Effects of large-scale disturbance on animal space use: Functional responses by greater sage-grouse after megafire: Ecology and Evolution, v. 13, no. 4, e9933, 30 p., https://doi.org/10.1002/ece3.9933.","productDescription":"e9933, 30 p.","ipdsId":"IP-136242","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":443914,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.9933","text":"Publisher Index Page"},{"id":429874,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.30314841246594,\n 44.47748992705084\n ],\n [\n -112.30314841246594,\n 44.05252291063391\n ],\n [\n -111.2985432757669,\n 44.05252291063391\n ],\n [\n -111.2985432757669,\n 44.47748992705084\n ],\n [\n -112.30314841246594,\n 44.47748992705084\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"13","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-04-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Stevens, Bryan S.","contributorId":171809,"corporation":false,"usgs":false,"family":"Stevens","given":"Bryan","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":903006,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roberts, Shane","contributorId":279606,"corporation":false,"usgs":false,"family":"Roberts","given":"Shane","affiliations":[{"id":56023,"text":"idfg","active":true,"usgs":false}],"preferred":false,"id":903007,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Conway, Courtney J. 0000-0003-0492-2953 cconway@usgs.gov","orcid":"https://orcid.org/0000-0003-0492-2953","contributorId":2951,"corporation":false,"usgs":true,"family":"Conway","given":"Courtney","email":"cconway@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903008,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Engelstead, Devin K.","contributorId":338188,"corporation":false,"usgs":false,"family":"Engelstead","given":"Devin","email":"","middleInitial":"K.","affiliations":[{"id":37086,"text":"U.S. Bureau of Land Management","active":true,"usgs":false}],"preferred":false,"id":903009,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70244272,"text":"70244272 - 2023 - Assessing large landscape patterns of potential fire connectivity using circuit methods","interactions":[],"lastModifiedDate":"2023-06-12T11:24:05.990292","indexId":"70244272","displayToPublicDate":"2023-04-07T06:17:36","publicationYear":"2023","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":"Assessing large landscape patterns of potential fire connectivity using circuit methods","docAbstract":"Minimizing negative impacts of wildfire is a major societal objective in fire-prone landscapes. Models of fire connectivity can aid in understanding and managing wildfires by analyzing potential fire spread and conductance patterns. We define ‘fire connectivity’ as the landscape’s capacity to facilitate fire transmission from one point on the landscape to another.
Our objective was to develop an approach for modeling fire connectivity patterns representing potential fire spread and relative flow across a broad landscape extent, particularly in the management-relevant context of fuel breaks.
We applied an omnidirectional circuit theory algorithm to model fire connectivity in the Great Basin of the western United States. We used predicted rates of fire spread to approximate conductance and calculated current densities to identify connections among areas with high spread rates. We compared existing and planned fuel breaks with fire connectivity patterns.
Fire connectivity and relative flow outputs were characterized by spatial heterogeneity in the landscape’s capacity to transmit fire. We found that existing fuel break networks were denser in areas with relatively diffuse and impeded flow patterns, rather than in locations with channelized flow.
This approach could be paired with traditional fire behavior and risk analyses to better understand wildfire spread as well as direct strategic placement of individual fuel breaks within larger networks to constrain fire spread. Thus, our findings may offer local- to landscape-level support for management actions that aim to disrupt fire spread and mitigate the costs of fire on the landscape.
","language":"English","publisher":"Springer","doi":"10.1007/s10980-022-01581-y","usgsCitation":"Buchholtz, E.K., Kreitler, J.R., Shinneman, D.J., Crist, M., and Heinrichs, J., 2023, Assessing large landscape patterns of potential fire connectivity using circuit methods: Landscape Ecology, v. 38, p. 1663-1676, https://doi.org/10.1007/s10980-022-01581-y.","productDescription":"14 p.","startPage":"1663","endPage":"1676","ipdsId":"IP-138309","costCenters":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":443924,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10980-022-01581-y","text":"Publisher Index Page"},{"id":435384,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9EA3E00","text":"USGS data release","linkHelpText":"Circuit-based potential fire connectivity and relative flow patterns in the Great Basin, United States, 270 meters"},{"id":417995,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Idaho, Nevada, Oregon, Utah","otherGeospatial":"Great Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.51991830833018,\n 44.189247821751025\n ],\n [\n -121.51991830833018,\n 37.1713133111553\n ],\n [\n -110.58222834230502,\n 37.1713133111553\n ],\n [\n -110.58222834230502,\n 44.189247821751025\n ],\n [\n -121.51991830833018,\n 44.189247821751025\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"38","noUsgsAuthors":false,"publicationDate":"2023-04-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Buchholtz, Erin K. 0000-0002-1985-9531","orcid":"https://orcid.org/0000-0002-1985-9531","contributorId":300162,"corporation":false,"usgs":true,"family":"Buchholtz","given":"Erin","middleInitial":"K.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":875111,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kreitler, Jason R. 0000-0002-0243-5281 jkreitler@usgs.gov","orcid":"https://orcid.org/0000-0002-0243-5281","contributorId":4050,"corporation":false,"usgs":true,"family":"Kreitler","given":"Jason","email":"jkreitler@usgs.gov","middleInitial":"R.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":875112,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shinneman, Douglas J. 0000-0002-4909-5181 dshinneman@usgs.gov","orcid":"https://orcid.org/0000-0002-4909-5181","contributorId":147745,"corporation":false,"usgs":true,"family":"Shinneman","given":"Douglas","email":"dshinneman@usgs.gov","middleInitial":"J.","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":875113,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Crist, Michele R.","contributorId":178453,"corporation":false,"usgs":false,"family":"Crist","given":"Michele R.","affiliations":[],"preferred":false,"id":875114,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Heinrichs, Julie A. 0000-0001-7733-5034","orcid":"https://orcid.org/0000-0001-7733-5034","contributorId":240888,"corporation":false,"usgs":false,"family":"Heinrichs","given":"Julie A.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":875115,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70243875,"text":"70243875 - 2023 - Time-lapse seafloor surveys reveal how turbidity currents and internal tides in Monterey Canyon interact with the seabed at centimeter-scale","interactions":[],"lastModifiedDate":"2023-05-24T17:02:25.974335","indexId":"70243875","displayToPublicDate":"2023-04-06T11:56:41","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5739,"text":"Journal of Geophysical Research: Earth Surface","onlineIssn":"2169-9011","active":true,"publicationSubtype":{"id":10}},"title":"Time-lapse seafloor surveys reveal how turbidity currents and internal tides in Monterey Canyon interact with the seabed at centimeter-scale","docAbstract":"Here we show how ultra-high resolution seabed mapping using new technology can help to understand processes that sculpt submarine canyons. Time-lapse seafloor surveys were conducted in the axis of Monterey Canyon, ∼50 km from the canyon head (∼1,840 m water depth) over an 18-month period. These surveys comprised 5-cm resolution multibeam bathymetry, 1-cm resolution lidar bathymetry, and 2-mm resolution stereophotographic imagery. Bathymetry data reveal centimeter-scale textures that would be undetectable by more traditional survey methods. Upward-looking Acoustic Doppler Current Profilers at the site recorded the flow character of internal tides and the passage of three turbidity currents, while sediment cores collected from the site record flow deposits. Combined with flow and core data, the bathymetry shows how turbidity currents and internal tides modify the seabed. The turbidity currents drape sediment across the site, infilling bedform troughs and smoothing erosional features carved by the internal tides (e.g., rippled scours). Turbidity currents with speeds of 0.9–3.3 m/s failed to cause notable bedform movement, which is surprising given that flows with similar speeds produced rapid bedform migration elsewhere, including the upper Monterey Canyon. The lack of migration may be related to the character of the underlying substrate or indicate that turbidity currents at the site lack dense, near-bed layers. The scale of scours produced by the internal tides (≤0.7 m/s) approaches the scale of features recorded in the ancient rock record. Thus, these results illustrate how the scale gap between seabed mapping technology and the rock record may eventually be bridged.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JF006705","usgsCitation":"Wolfson-Schwehr, M., Paull, C.K., Caress, D.W., Gwiazda, R., Nieminski, N.M., Talling, P.J., Carvajal, C., Simmons, S.M., and Troni, G., 2023, Time-lapse seafloor surveys reveal how turbidity currents and internal tides in Monterey Canyon interact with the seabed at centimeter-scale: Journal of Geophysical Research: Earth Surface, v. 128, no. 4, e2022JF006705, 22 p., https://doi.org/10.1029/2022JF006705.","productDescription":"e2022JF006705, 22 p.","ipdsId":"IP-136210","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":443928,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022jf006705","text":"Publisher Index Page"},{"id":417402,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Monterey Canyon","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.095833,\n 36.704167\n ],\n [\n -122.095833,\n 36.7\n ],\n [\n -122.0875,\n 36.7\n ],\n [\n -122.0875,\n 36.704167\n ],\n [\n -122.095833,\n 36.704167\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"128","issue":"4","noUsgsAuthors":false,"publicationDate":"2023-04-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Wolfson-Schwehr, Monica","contributorId":175112,"corporation":false,"usgs":false,"family":"Wolfson-Schwehr","given":"Monica","email":"","affiliations":[],"preferred":false,"id":873579,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Paull, Charles K. 0000-0001-5940-3443","orcid":"https://orcid.org/0000-0001-5940-3443","contributorId":55825,"corporation":false,"usgs":false,"family":"Paull","given":"Charles","email":"","middleInitial":"K.","affiliations":[{"id":7043,"text":"University of North Carolina","active":true,"usgs":false}],"preferred":true,"id":873580,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Caress, David W.","contributorId":147392,"corporation":false,"usgs":false,"family":"Caress","given":"David","email":"","middleInitial":"W.","affiliations":[{"id":16837,"text":"MBARI","active":true,"usgs":false}],"preferred":false,"id":873581,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gwiazda, Roberto","contributorId":147193,"corporation":false,"usgs":false,"family":"Gwiazda","given":"Roberto","email":"","affiliations":[{"id":13620,"text":"Monterey Bay Aquarium Research Institute, Moss Landing, California","active":true,"usgs":false}],"preferred":false,"id":873582,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nieminski, Nora Maria 0000-0002-4465-8731","orcid":"https://orcid.org/0000-0002-4465-8731","contributorId":279764,"corporation":false,"usgs":true,"family":"Nieminski","given":"Nora","email":"","middleInitial":"Maria","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":873583,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Talling, Peter J.","contributorId":195515,"corporation":false,"usgs":false,"family":"Talling","given":"Peter","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":873584,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Carvajal, Cristian","contributorId":204133,"corporation":false,"usgs":false,"family":"Carvajal","given":"Cristian","email":"","affiliations":[{"id":16837,"text":"MBARI","active":true,"usgs":false}],"preferred":false,"id":873585,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Simmons, Stephen M.","contributorId":305699,"corporation":false,"usgs":false,"family":"Simmons","given":"Stephen","email":"","middleInitial":"M.","affiliations":[{"id":40174,"text":"University of Hull","active":true,"usgs":false}],"preferred":false,"id":873586,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Troni, Giancarlo","contributorId":305700,"corporation":false,"usgs":false,"family":"Troni","given":"Giancarlo","email":"","affiliations":[{"id":66274,"text":"Pontifica Universidad Catolica de Chile","active":true,"usgs":false}],"preferred":false,"id":873587,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70242657,"text":"70242657 - 2023 - Knowledge coproduction on the impact of decisions for waterbird habitat in a changing climate","interactions":[],"lastModifiedDate":"2023-10-11T15:17:00.654848","indexId":"70242657","displayToPublicDate":"2023-04-06T06:58:42","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1321,"text":"Conservation Biology","active":true,"publicationSubtype":{"id":10}},"title":"Knowledge coproduction on the impact of decisions for waterbird habitat in a changing climate","docAbstract":"Scientists, resource managers, and decision-makers increasingly use knowledge co-production to guide the stewardship of future landscapes under climate change. This process was applied in the California Central Valley, USA to solve complex conservation problems, where managed wetlands and croplands are flooded between fall and spring to support some of the largest concentrations of shorebirds and waterfowl in the world. We co-produced scenario narratives, spatially-explicit flooded waterbird habitat models, data products, and new knowledge about climate adaptation potential. We document our co-production process, and using the co-produced models, we ask: “when and where do management actions make a difference?” and “when does climate override these actions?” The outcomes of this process provide lessons learned on how to co-create usable information and how to increase climate adaptive capacity in a highly managed landscape. We found that: 1) actions to restore wetlands and prioritize their water supply create habitat outcomes resilient to climate change impacts particularly in March, when habitat is most limited, 2) land protection combined with management can increase the ecosystem's resilience to climate change, and 3) the uptake and use of this information was influenced by the roles of different stakeholders, plus rapidly changing water policies, discrepancies in decision-making time frames, and immediate crises of extreme drought. While a broad stakeholder group contributed knowledge to scenario narratives and model development, to co-produce usable information, data products were tailored to a small set of decision contexts, leading to fewer stakeholder participants over time. A boundary organization convened stakeholders across a large landscape, and early adopters helped to build legitimacy, yet broad-scale use of climate adaptation knowledge will depend on state and local policies, engagement with decision-makers that have legislative and budgetary authority, and the capacity to fit data products to specific decision needs.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/cobi.14089","usgsCitation":"Byrd, K.B., Matchett, E., Mengelt, C., Wilson, T., DiPietro, D., Moritsch, M., Conlisk, E., Veloz, S., Casazza, M.L., and Reiter, M., 2023, Knowledge coproduction on the impact of decisions for waterbird habitat in a changing climate: Conservation Biology, v. 37, no. 5, e14089, 12 p., https://doi.org/10.1111/cobi.14089.","productDescription":"e14089, 12 p.","ipdsId":"IP-145413","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":499259,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/cobi.14089","text":"Publisher Index Page"},{"id":415649,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.5,\n 39.75\n ],\n [\n -122.5,\n 35.5\n ],\n [\n -118.5,\n 35.5\n ],\n [\n -118.5,\n 39.75\n ],\n [\n -122.5,\n 39.75\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"37","issue":"5","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Byrd, Kristin B. 0000-0002-5725-7486 kbyrd@usgs.gov","orcid":"https://orcid.org/0000-0002-5725-7486","contributorId":3814,"corporation":false,"usgs":true,"family":"Byrd","given":"Kristin","email":"kbyrd@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":869232,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Matchett, Elliott 0000-0001-5095-2884 ematchett@usgs.gov","orcid":"https://orcid.org/0000-0001-5095-2884","contributorId":5541,"corporation":false,"usgs":true,"family":"Matchett","given":"Elliott","email":"ematchett@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":869233,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mengelt, Claudia 0000-0001-7869-5170","orcid":"https://orcid.org/0000-0001-7869-5170","contributorId":304087,"corporation":false,"usgs":true,"family":"Mengelt","given":"Claudia","email":"","affiliations":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true}],"preferred":true,"id":869234,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wilson, Tamara 0000-0001-7399-7532 tswilson@usgs.gov","orcid":"https://orcid.org/0000-0001-7399-7532","contributorId":2975,"corporation":false,"usgs":true,"family":"Wilson","given":"Tamara","email":"tswilson@usgs.gov","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":869235,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"DiPietro, Deanne","contributorId":304089,"corporation":false,"usgs":false,"family":"DiPietro","given":"Deanne","email":"","affiliations":[{"id":38279,"text":"Conservation Biology Institute","active":true,"usgs":false}],"preferred":false,"id":869236,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Moritsch, Monica","contributorId":304091,"corporation":false,"usgs":false,"family":"Moritsch","given":"Monica","affiliations":[{"id":65966,"text":"EDF","active":true,"usgs":false}],"preferred":false,"id":869237,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Conlisk, Erin","contributorId":304092,"corporation":false,"usgs":false,"family":"Conlisk","given":"Erin","affiliations":[{"id":17734,"text":"Point Blue Conservation Science","active":true,"usgs":false}],"preferred":false,"id":869238,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Veloz, Sam","contributorId":304093,"corporation":false,"usgs":false,"family":"Veloz","given":"Sam","affiliations":[{"id":17734,"text":"Point Blue Conservation Science","active":true,"usgs":false}],"preferred":false,"id":869239,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Casazza, Michael L. 0000-0002-5636-735X mike_casazza@usgs.gov","orcid":"https://orcid.org/0000-0002-5636-735X","contributorId":2091,"corporation":false,"usgs":true,"family":"Casazza","given":"Michael","email":"mike_casazza@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":869240,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Reiter, Matthew","contributorId":304094,"corporation":false,"usgs":false,"family":"Reiter","given":"Matthew","affiliations":[{"id":17734,"text":"Point Blue Conservation Science","active":true,"usgs":false}],"preferred":false,"id":869241,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70242706,"text":"70242706 - 2023 - Shallow deformation on the Kirby Hills fault, Sacramento–San Joaquin Delta, California (USA), revealed from high-resolution seismic reflection data and coring in a fluvial system","interactions":[],"lastModifiedDate":"2023-06-09T15:15:59.382116","indexId":"70242706","displayToPublicDate":"2023-04-06T06:51:28","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1820,"text":"Geosphere","active":true,"publicationSubtype":{"id":10}},"title":"Shallow deformation on the Kirby Hills fault, Sacramento–San Joaquin Delta, California (USA), revealed from high-resolution seismic reflection data and coring in a fluvial system","docAbstract":"The Sacramento–San Joaquin Delta (Delta) in California (USA) is an important part of the state’s freshwater system and is also a major source of agricultural and natural resources. However, the Delta is traversed by a series of faults that make up the easternmost part of the San Andreas fault system at this latitude and pose seismic hazard to this region. In this study, we use new high-resolution chirp subbottom data to map and characterize the shallow expression of the Kirby Hills fault, where it has been mapped to cross the Sacramento River at the western extent of the Delta. The fault is buried here, but we document a broad zone of deformation associated with the eastern strand of the fault that changes in character, along strike, across ~600 m of the river channel. Radiocarbon dates from sediment cores collected in the Sacramento River provide some minimum constraints on the age of deformation. We do not observe evidence of the western strand as previously mapped. We also discuss difficulties of conducting a paleoseismologic study in a fluvial environment.
Groundwater is critical for maintaining stream baseflow and thermal stability; however, the influence of groundwater on streamflow has been difficult to evaluate at broad spatial scales. Techniques such as baseflow separation necessitate streamflow records and do not directly indicate whether groundwater inflow may be sourced from more dynamic shallow flowpaths. We present a web tool application PASTA (Paired Air and Stream Temperature Analysis; https://cuahsi.shinyapps.io/pasta/) that capitalizes on increased public stream temperature data availability and large-scale, gridded climate observations to provide new and efficient insights regarding relative groundwater influence on streams. PASTA analyzes paired air and stream water temperature signals to evaluate spatiotemporal patterns in stream thermal sensitivity and relative groundwater influence, including inference regarding the dominant source groundwater depth (shallow or deep (i.e., approximately >6 m depth)). The tool is linked to publicly available stream temperature datasets and accepts user-uploaded datasets. As local air temperature is not often monitored, PASTA pulls daily air temperature data from the comprehensive Daymet products when directly measured data are unavailable, allowing the repurposing of existing stream temperature data. After data are selected or uploaded, the tool (a) fits sinusoidal curves of daily stream and air temperatures by year (water or calendar) to indicate groundwater influence characteristics and (b) performs linear regressions for stream versus air temperatures to indicate stream thermal sensitivity. Results are exported in ASCII file format, creating an efficient and approachable analysis tool for the adoption of newly developed heat tracing analysis from stream reach to landscape scales.
The presence of taliks (perennially unfrozen zones in permafrost areas) adversely affects the thermal stability of infrastructure in cold regions, including roads. The role of heat advection on talik development and feedback on permafrost degradation has not been quantified methodically in this context. We incorporate a surface energy balance model into a coupled groundwater flow and energy transport numerical model (SUTRA-ice). The model, calibrated with long-term observations (1997–2018 on the Alaska Highway), is used to investigate and quantify the role of heat advection on talik initiation and development under a road embankment. Over the 25-year simulation period, the new model is driven by reconstructed meteorological data and has a good agreement with near surface soil temperatures. The model successfully reproduces the increasing depth to the permafrost table (mean absolute error <0.2 m), and talik development. The results demonstrate that heat advection provides an additional energy source that expedites the rate of permafrost thaw and roughly doubles the rate of permafrost table deepening, compared to purely conductive thawing. Talik initially formed and grew over time under the combined effect of water flow, snow insulation, road construction and climate warming. Talik formation creates a new thermal state under the road embankment, resulting in acceleration of underlying permafrost degradation, due to the positive feedback of heat accumulation created by trapped unfrozen water. In a changing climate, mobile water flow will play a more important role in permafrost thaw and talik development under road embankments, and is likely to significantly increase maintenance costs and reduce the long-term stability of the infrastructure.
Acoustic telemetry is a commonly used technology to monitor animal occupancy and infer movement in aquatic environments. The information that acoustic telemetry provides is vital for spatial planning and management decisions concerning aquatic and coastal environments by characterizing behaviors and habitats such as spawning aggregations, migrations, corridors, and nurseries, among others. However, performance of acoustic telemetry equipment and resulting detection ranges and efficiencies can vary as a function of environmental conditions, leading to potentially biased interpretations of telemetry data. Here, we characterize variation in detection performance using an acoustic telemetry receiver array deployed in Wellfleet Harbor, Massachusetts, USA from 2015 to 2017. The array was designed to study benthic invertebrate movements and provided an in situ opportunity to identify factors driving variation in detection probability.
The near-shore location proximate to environmental monitoring allowed for a detailed examination of factors influencing detection efficiency in a range-testing experiment. Detection ranges varied from < 50 to 1,500 m and efficiencies varied from 0 to 100% within those detection ranges. Detection efficiency was affected by distance, wind speed and direction, wave height and direction, water temperature, water depth, and water quality.
Performance of acoustic telemetry systems is strongly contingent on environmental conditions. Our study found that wind, waves, water temperature, water quality, and depth all affected performance to an extent that could seriously compromise a study if these effects were not taken into consideration. Other unmeasured factors may also be important, depending on the characteristics of each site. This information can help guide future telemetry study designs by helping researchers anticipate the density of receivers required to achieve study objectives. Researchers can further refine and document the reliability of their data by incorporating continuously deployed range-testing tags and prior knowledge on varying detection efficiency into movement and occupancy models.
","language":"English","publisher":"Springer","doi":"10.1186/s40317-023-00317-2","usgsCitation":"Long, M., Jordaan, A., and Castro-Santos, T.R., 2023, Environmental factors influencing detection efficiency of an acoustic telemetry array and consequences for data interpretation: Animal Biotelemetry, v. 11, 18, 13 p., https://doi.org/10.1186/s40317-023-00317-2.","productDescription":"18, 13 p.","ipdsId":"IP-141767","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":443940,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s40317-023-00317-2","text":"Publisher Index Page"},{"id":416951,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Massachusetts","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -70.12152378095689,\n 41.97721573790295\n ],\n [\n -70.12152378095689,\n 41.80349781857885\n ],\n [\n -69.90189169540182,\n 41.80349781857885\n ],\n [\n -69.90189169540182,\n 41.97721573790295\n ],\n [\n -70.12152378095689,\n 41.97721573790295\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"11","noUsgsAuthors":false,"publicationDate":"2023-04-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Long, Michael 0000-0001-6735-6878","orcid":"https://orcid.org/0000-0001-6735-6878","contributorId":261905,"corporation":false,"usgs":false,"family":"Long","given":"Michael","email":"","affiliations":[{"id":34616,"text":"University of Massachusetts Amherst","active":true,"usgs":false}],"preferred":false,"id":872227,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jordaan, Adrian","contributorId":257709,"corporation":false,"usgs":false,"family":"Jordaan","given":"Adrian","affiliations":[{"id":37201,"text":"UMass Amherst","active":true,"usgs":false}],"preferred":false,"id":872228,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Castro-Santos, Theodore R. 0000-0003-2575-9120 tcastrosantos@usgs.gov","orcid":"https://orcid.org/0000-0003-2575-9120","contributorId":3321,"corporation":false,"usgs":true,"family":"Castro-Santos","given":"Theodore","email":"tcastrosantos@usgs.gov","middleInitial":"R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":872229,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70242052,"text":"fs20233015 - 2023 - Landsat Collection 2 U.S. Analysis Ready Data","interactions":[],"lastModifiedDate":"2023-04-06T10:56:36.56528","indexId":"fs20233015","displayToPublicDate":"2023-04-05T14:05:45","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-3015","displayTitle":"Landsat Collection 2 U.S. Analysis Ready Data","title":"Landsat Collection 2 U.S. Analysis Ready Data","docAbstract":"Landsat Collection 2 (C2) U.S. Analysis Ready Data (U.S. ARD) are bundles of tiled Landsat data that make the Landsat archive easier to analyze and reduce the amount of time users spend on data processing for time-series analysis. Landsat C2 was released in 2020 and includes improvements over Landsat Collection 1 data, including better geometric accuracy, which increases the number of available C2 U.S. ARD tiles. Landsat C2 U.S. ARD are processed to the highest scientific standards.
Landsat C2 U.S. ARD are available for the conterminous United States (1982–present), Alaska (1984–present), and Hawaii (1989–93 and 1999–present) using Landsat C2 Level-1 data processed into Albers Equal-Area Conic-projected Level-2 surface reflectance and surface temperature products.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233015","usgsCitation":"U.S. Geological Survey, 2023, Landsat Collection 2 U.S. Analysis Ready Data: U.S. Geological Survey Fact Sheet 2023–3015, 2 p., https://doi.org/10.3133/fs20233015.","productDescription":"2 p.","numberOfPages":"2","ipdsId":"IP-145239","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":415262,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3015/coverthb.jpg"},{"id":415264,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3015/fs20233015.pdf","text":"Report","size":"2.10 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023–3015"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"geometry\": {\n \"type\": 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47914 252nd Street
Sioux Falls, SD 57198
Salinity is known to affect drinking-water supplies and damage irrigated agricultural lands. Selenium in high concentrations is harmful to fish and other wildlife. Land managers, water providers, and agricultural producers in the lower Gunnison River Basin in western Colorado expend resources mitigating the effects of these constituents. The U.S. Geological Survey revised existing salinity (total dissolved solids) and selenium models for the lower Gunnison River Basin in an attempt to better identify areas of greatest salinity and selenium yield. This effort developed maps of yields predicted from multiple linear regression (MLR) models for the lower Gunnison River Basin. The models included data for irrigation and nonirrigation seasons and two periods, 1992–2004 and 2005–13.
Concentrations of salinity and selenium and discharge measurements made at the time of sampling were used to compute loads for subbasins (component drainages of the larger lower Gunnison River Basin study area), which were adjusted for inflows and outflows of canal loads. Load regression equations were determined from explanatory basin characteristics that included physical properties, precipitation, land use and cover, surficial deposits (soil and unconsolidated geologic materials), and bedrock geology. Loads of salinity and selenium were converted to yields by using the subbasin drainage areas, and an empirical Bayesian kriging procedure was used to produce robust grids of yields for salinity and selenium.
Salinity yields ranged from 0.00667 to 6.564 tons per year per acre. The highest salinity yields, greater than about 5.0 tons per year per acre, are predicted on the western side of the Uncompahgre River upstream from Delta, Colorado, an area with a high density of irrigated land. The selenium yield map shows a similar pattern, but the highest yields are somewhat more confined to the eastern side of the lower Uncompahgre River and south of the Gunnison River near the confluence with the Uncompahgre River at Delta, Colorado. Selenium yields ranged from 2.6888 x 10-10 to 0.000445 pounds per day per acre. The highest predicted selenium yields, greater than 0.0003 pounds per day per acre, were in the area downstream from Montrose, Colorado, on the eastern side of the Uncompahgre River.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20235013","collaboration":"Prepared in cooperation with the Bureau of Reclamation and the Colorado Water Conservation Board","usgsCitation":"Williams, C.A., Gidley, R.G., and Stevens, M.R., 2023, Salinity and selenium yield maps derived from geostatistical modeling in the lower Gunnison River Basin, western Colorado, 1992–2013: U.S. Geological Survey Scientific Investigations Report 2023–5013, 37 p., https://doi.org/10.3133/sir20235013.","productDescription":"Report: vi, 37 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-127438","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":415136,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CW7Q1N","text":"USGS data release","linkHelpText":"Basin Characteristics and Salinity and Selenium Loads and Yields for Selected Subbasins in the Lower Gunnison River Basin, Western Colorado, 1992─2013"},{"id":415232,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2023/5013/images"},{"id":415233,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2023/5013/sir20235013.xml"},{"id":415255,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sir20235013/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2023-5013"},{"id":415135,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2023/5013/sir20235013.pdf","text":"Report","size":"13.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2023-5013"},{"id":415134,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2023/5013/coverthb.jpg"},{"id":415137,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS data release","linkHelpText":"USGS water data for the Nation: U.S. Geological Survey National Water Information System database"},{"id":500707,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114651.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Colorado","otherGeospatial":"Lower Gunnison River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -108.66041487616633,\n 38.99638415429618\n ],\n [\n -108.6616273692395,\n 39.00186401594732\n ],\n [\n -108.6616273692395,\n 38.9967055439632\n ],\n [\n -108.6881779535584,\n 38.77194822283556\n ],\n [\n -108.58197561628282,\n 38.556862390281765\n ],\n [\n -108.19035825380965,\n 38.16912358075567\n ],\n [\n -108.12730061605224,\n 37.915589610235415\n ],\n [\n -107.96135946405924,\n 37.80029529902727\n ],\n [\n -107.76886772774775,\n 37.81078405090291\n ],\n [\n -107.61846017361093,\n 38.26998042097301\n ],\n [\n -107.25524694190712,\n 38.623585049765126\n ],\n [\n -107.0055365452011,\n 38.85181039167898\n ],\n [\n -106.90464345562309,\n 38.99114000809618\n ],\n [\n -106.95004534593326,\n 39.07538965450817\n ],\n [\n -107.02178619634307,\n 39.241636232003856\n ],\n [\n -107.71852594996861,\n 39.17165133541914\n ],\n [\n -108.1185061789019,\n 39.03147242299278\n ],\n [\n -108.37655793950398,\n 38.98134099584442\n ],\n [\n -108.55719417192573,\n 39.05652481206508\n ],\n [\n -108.66041487616633,\n 38.99638415429618\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, Mail Stop 415
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Outside the United States and Canada, most of the world’s supplies of oil and natural gas are recovered from conventional (or discrete) oil and gas accumulations. This type of hydrocarbon accumulation remains a target for exploration. In this report, exploration and discovery data are used to visually assist in describing the exploration maturity of selected petroleum provinces with respect to conventional oil and natural gas accumulations. The specific provinces are the Campos Basin (Brazil), the Santos Basin (Brazil), the North Sea Graben (northwestern Europe), the Middle Magdelena Basin (Colombia), the Sirte Basin (Libya), and the Kutei Basin (Indonesia). For each province, discovery data and well data through October 2019 are reported; from these data, depth distributions of the oil in oil fields and natural gas in gas fields were computed.
The concepts of delineated prospective area and explored area include elements of geographic spatial information and statistical data analytics. Graphs showing dynamic growth of discoveries that are tied to the delineated prospective area provide a means of grading prospective area. Visualizations put the results of exploration in the context of geographic and geologic features of the play or basin and can be a tool to assist geologists with the appraisal of the number and sizes of undiscovered petroleum accumulations. Visualizations of exploration drilling and discoveries can (1) assist in conceptualizing a geologic model of the basin, (2) highlight relations among discovered accumulations in different plays or assessment units within the basin, and (3) allow the geologist to identify the missing information needed to complete the geologic model of a basin. Further, if visualization attributes can be quantified, they may be used for formulating quantitative models that predict numbers and sizes of undiscovered oil and gas accumulations. Such modeling approaches include discovery process models, Bayesian network models that characterize play or assessment unit dependencies, and innovative applications of machine learning to complement standard geologic assessments.
The purpose of this report is to show how visualizations can further the understanding of exploration maturity for the six selected petroleum provinces. It also shows how the geologic framework, geologic data, and drilling and discovery trends can give context to the interpretation of the visualizations that lead to appraisal of exploration maturity.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20235010","usgsCitation":"Attanasi, E.D., and Freeman, P.A., 2023, Visualization of petroleum exploration maturity for six petroleum provinces outside the United States and Canada: U.S. Geological Survey Scientific Investigations Report 2023–5010, 38 p., https://doi.org/10.3133/sir20235010.","productDescription":"viii, 38 p.","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-119047","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":414671,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20235010/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 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U.S. Geological Survey
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Age-related variation in foraging performance can result from both within-individual change and selection processes. These mechanisms can only be disentangled by using logistically challenging long-term, longitudinal studies. Coupling a long-term demographic data set with high-temporal-resolution tracking of 18 Adélie penguins (Pygoscelis adeliae, age 4–15 yrs old) over three consecutive annual cycles, we examined how foraging behaviour changed within individuals of different age classes. Evidence indicated within-individual improvement in young and middle-age classes, but a significant decrease in foraging dive frequency within old individuals, associated with a decrease in the dive descent rate. Decreases in foraging performance occurred at a later age (from 12–15 yrs old to 15–18 yrs old) than the onset of senescence predicted for this species (9–11 yrs old). Foraging dive frequency was most affected by the interaction between breeding status and annual life-cycle periods, with frequency being highest during returning migration and breeding season and was highest overall for successful breeders during the chick-rearing period. Females performed more foraging dives per hour than males. This longitudinal, full annual cycle study allowed us to shed light on the changes in foraging performance occurring among individuals of different age classes and highlighted the complex interactions among drivers of individual foraging behaviour.
","language":"English","publisher":"The Royal Society Publishing","doi":"10.1098/rspb.2022.2480","usgsCitation":"Lescroël, A., Schmidt, A., Ainley, D., Dugger, K., Elrod, M., Jongsomjit, D., Morandini, V., Winquist, S., and Ballard, G., 2023, High-resolution recording of foraging behaviour over multiple annual cycles shows decline in old Adélie penguins’ performance: Proceedings of the Royal Society B, v. 290, no. 1996, 20222480, 10 p., https://doi.org/10.1098/rspb.2022.2480.","productDescription":"20222480, 10 p.","ipdsId":"IP-147917","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":443946,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10261/309804","text":"External Repository"},{"id":430010,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"290","issue":"1996","noUsgsAuthors":false,"publicationDate":"2023-04-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Lescroël, Amélie","contributorId":338339,"corporation":false,"usgs":false,"family":"Lescroël","given":"Amélie","affiliations":[{"id":17734,"text":"Point Blue Conservation Science","active":true,"usgs":false}],"preferred":false,"id":903186,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schmidt, Annie","contributorId":338340,"corporation":false,"usgs":false,"family":"Schmidt","given":"Annie","affiliations":[{"id":17734,"text":"Point Blue Conservation Science","active":true,"usgs":false}],"preferred":false,"id":903187,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ainley, David G.","contributorId":338341,"corporation":false,"usgs":false,"family":"Ainley","given":"David G.","affiliations":[{"id":81117,"text":"H. T. Harvey & Associates Ecological Consultants","active":true,"usgs":false}],"preferred":false,"id":903188,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Dugger, Katie M. 0000-0002-4148-246X cdugger@usgs.gov","orcid":"https://orcid.org/0000-0002-4148-246X","contributorId":4399,"corporation":false,"usgs":true,"family":"Dugger","given":"Katie","email":"cdugger@usgs.gov","middleInitial":"M.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":903189,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Elrod, Megan","contributorId":338342,"corporation":false,"usgs":false,"family":"Elrod","given":"Megan","affiliations":[{"id":17734,"text":"Point Blue Conservation Science","active":true,"usgs":false}],"preferred":false,"id":903190,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jongsomjit, Dennis","contributorId":338343,"corporation":false,"usgs":false,"family":"Jongsomjit","given":"Dennis","affiliations":[{"id":17734,"text":"Point Blue Conservation Science","active":true,"usgs":false}],"preferred":false,"id":903191,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Morandini, Virginia","contributorId":338344,"corporation":false,"usgs":false,"family":"Morandini","given":"Virginia","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":903192,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Winquist, Suzanne","contributorId":338345,"corporation":false,"usgs":false,"family":"Winquist","given":"Suzanne","email":"","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":903193,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ballard, Grant","contributorId":338346,"corporation":false,"usgs":false,"family":"Ballard","given":"Grant","affiliations":[{"id":17734,"text":"Point Blue Conservation Science","active":true,"usgs":false}],"preferred":false,"id":903194,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70243115,"text":"70243115 - 2023 - Assessment of three methods to evaluate the distribution of submersed aquatic vegetation in western Lake Erie","interactions":[],"lastModifiedDate":"2023-05-01T12:29:38.603703","indexId":"70243115","displayToPublicDate":"2023-04-05T07:26:06","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1919,"text":"Hydrobiologia","onlineIssn":"1573-5117","printIssn":"0018-8158","active":true,"publicationSubtype":{"id":10}},"title":"Assessment of three methods to evaluate the distribution of submersed aquatic vegetation in western Lake Erie","docAbstract":"Submersed aquatic vegetation (SAV) plays an important role in ecosystems. Inventories of SAV spatial distribution and composition are important for monitoring changes in SAV. In this study, we compared three common SAV sampling methods to quantify SAV in western Lake Erie. Aerial imagery of near-shore areas in western Lake Erie was classified using object-based image analysis (OBIA) and evaluated against field-based surveys using single-beam sonar or rake samples. To assess variation among methods, data were assigned either vegetation ‘presence’ or ‘absence’ and compared for simple correspondence and agreement (Cohen’s Kappa, κ). The two field-based methods had the highest correspondence at 78% (n = 782) and the highest κ = 0.545. Correspondence between OBIA and rake surveys was 69% (n = 245) and κ = 0.36. Correspondence between OBIA and hydroacoustics was the lowest of 54% (n = 30,768) with an agreement of κ = 0.17. Environmental factors such as water turbidity may have played a role in reduced agreement between OBIA and field methods. Determining the optimal method or combination of methods will depend upon research goals, effort, and cost, but each method can provide reliable SAV information for resource management.
Some mammal species inhabiting high-latitude biomes have evolved a seasonal moulting pattern that improves camouflage via white coats in winter and brown coats in summer. In many high-latitude and high-altitude areas, the duration and depth of snow cover has been substantially reduced in the last five decades. This reduction in depth and duration of snow cover may create a mismatch between coat colour and colour of the background environment, and potentially reduce the survival rate of species that depend on crypsis. We used long-term (1977–2020) field data and capture–mark–recapture models to test the hypothesis that whiteness of the coat influences winter apparent survival in a cyclic population of snowshoe hares (Lepus americanus) at Kluane, Yukon, Canada. Whiteness of the snowshoe hare coat in autumn declined during this study, and snowshoe hares with a greater proportion of whiteness in their coats in autumn survived better during winter. However, whiteness of the coat in spring did not affect subsequent summer survival. These results are consistent with the hypothesis that the timing of coat colour change in autumn can reduce overwinter survival. Because declines in cyclic snowshoe hare populations are strongly affected by low winter survival, the timing of coat colour change may adversely affect snowshoe hare population dynamics as climate change continues.
Model calibration consists of using experimental or field data to estimate the unknown parameters of a mathematical model. The presence of model discrepancy and measurement bias in the data complicates this task. Satellite interferograms, for instance, are widely used for calibrating geophysical models in geological hazard quantification. In this work, we used satellite interferograms to relate ground deformation observations to the properties of the magma chamber at K i¯lauea Volcano in Hawai‘i. We derived closed-form marginal likelihoods and implemented posterior sampling procedures that simultaneously estimate the model discrepancy of physical models, and the measurement bias from the atmospheric error in satellite interferograms. We found that model calibration by aggregating multiple interferograms and downsampling the pixels in the interferograms can reduce the computation complexity compared to calibration approaches based on multiple data sets. The conditions that lead to no loss of information from data aggregation and downsampling are studied. Simulation illustrates that both discrepancy and measurement bias can be estimated, and real applications demonstrate that modeling both effects helps obtain a reliable estimation of a physical model’s unobserved parameters and enhance its predictive accuracy. We implement the computational tools in the RobustCalibration package available on CRAN.
One of the key factors in obtaining precise and accurate 230Th ages of corals, especially for corals with ages less than a few thousand years, is the correction for non-radiogenic 230Th based on an initial 230Th/232Th value (230Th/232Th0). Studies that consider coral 230Th/232Th0 values in intertidal environments are limited, and it is in these environments that corals have Th concentrations 100–1000 times greater than open-ocean corals. Here we present 66 reconstructed U–Th isochrons of modern and Holocene Porites lutea and lobata corals from throughout the Sumatran forearc islands (SFI). Mean square of weighted deviates (MSWD) is used to evaluate the variabilities of 28 location-specific 230Th/232Th0 values before the regional mean. Results show no significant spatial difference between the means of 3.1 ± 3.2 × 10−6 for the northern SFI and 4.7 ± 1.3 × 10−6 for the southern SFI. Using the weighted mean of 4.3 ± 2.5 × 10−6 for all islands has the advantage that no data need be subjectively excluded in the calculation of reliable 230Th ages, where a local initial value is unknown. There were no clear centennial to millennial time-scale changes in 230Th/232Th0 in the past 2500 years. This study, therefore, suggests a reliable value of initial 230Th/232Th ratio in intertidal environments.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.quascirev.2023.108005","usgsCitation":"Chiang, H., Philibosian, B.E., Meltzner, A.J., Wu, C., Shen, C., Edwards, R.L., Chuang, C., Suwargadi, B.W., and Natawidjaja, D.H., 2023, Investigating spatio-temporal variability of initial 230Th/232Th in intertidal corals: Quaternary Science Reviews, v. 307, 108005, 11 p., https://doi.org/10.1016/j.quascirev.2023.108005.","productDescription":"108005, 11 p.","ipdsId":"IP-125955","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":443962,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.quascirev.2023.108005","text":"Publisher Index Page"},{"id":415422,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Indonesia","otherGeospatial":"Sumatran forearc islands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 94,\n 3.5\n ],\n [\n 94,\n -3.5\n ],\n [\n 103,\n -3.5\n ],\n [\n 103,\n 3.5\n ],\n [\n 94,\n 3.5\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"307","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Chiang, Hong-Wei","contributorId":193421,"corporation":false,"usgs":false,"family":"Chiang","given":"Hong-Wei","email":"","affiliations":[{"id":5110,"text":"Earth Observatory of Singapore, Nanyang Technological University","active":true,"usgs":false},{"id":27347,"text":"High-precision Mass Spectrometry and Environment Change Laboratory (HISPEC), Department of Geosciences, National Taiwan University","active":true,"usgs":false}],"preferred":false,"id":868667,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Philibosian, Belle E. 0000-0003-3138-4716","orcid":"https://orcid.org/0000-0003-3138-4716","contributorId":206110,"corporation":false,"usgs":true,"family":"Philibosian","given":"Belle","email":"","middleInitial":"E.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":868668,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Meltzner, Aron J.","contributorId":193419,"corporation":false,"usgs":false,"family":"Meltzner","given":"Aron","email":"","middleInitial":"J.","affiliations":[{"id":7218,"text":"California Institute of Technology","active":true,"usgs":false},{"id":5110,"text":"Earth Observatory of Singapore, Nanyang Technological University","active":true,"usgs":false}],"preferred":false,"id":868669,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wu, Chung-Che","contributorId":193422,"corporation":false,"usgs":false,"family":"Wu","given":"Chung-Che","email":"","affiliations":[{"id":27347,"text":"High-precision Mass Spectrometry and Environment Change Laboratory (HISPEC), Department of Geosciences, National Taiwan University","active":true,"usgs":false}],"preferred":false,"id":868670,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Shen, Chuan-Chou","contributorId":193424,"corporation":false,"usgs":false,"family":"Shen","given":"Chuan-Chou","email":"","affiliations":[{"id":27347,"text":"High-precision Mass Spectrometry and Environment Change Laboratory (HISPEC), Department of Geosciences, National Taiwan University","active":true,"usgs":false}],"preferred":false,"id":868671,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Edwards, R. Lawrence 0000-0002-7027-5881","orcid":"https://orcid.org/0000-0002-7027-5881","contributorId":223143,"corporation":false,"usgs":false,"family":"Edwards","given":"R.","email":"","middleInitial":"Lawrence","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":868672,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Chuang, Chih-Kai","contributorId":303947,"corporation":false,"usgs":false,"family":"Chuang","given":"Chih-Kai","email":"","affiliations":[{"id":30216,"text":"National Taiwan University","active":true,"usgs":false}],"preferred":false,"id":868673,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Suwargadi, Bambang W.","contributorId":150205,"corporation":false,"usgs":false,"family":"Suwargadi","given":"Bambang","email":"","middleInitial":"W.","affiliations":[{"id":17941,"text":"Indonesian Institute of Sciences","active":true,"usgs":false}],"preferred":false,"id":868674,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Natawidjaja, Danny H.","contributorId":150204,"corporation":false,"usgs":false,"family":"Natawidjaja","given":"Danny","email":"","middleInitial":"H.","affiliations":[{"id":17941,"text":"Indonesian Institute of Sciences","active":true,"usgs":false}],"preferred":false,"id":868675,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70241874,"text":"fs20233012 - 2023 - U.S. Geological Survey and Blackfeet Water Department Hydrologic Assessment of the Blackfeet Indian Reservation, Montana","interactions":[],"lastModifiedDate":"2026-02-06T21:58:06.716158","indexId":"fs20233012","displayToPublicDate":"2023-04-03T15:12:20","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-3012","displayTitle":"U.S. Geological Survey and Blackfeet Water Department Hydrologic Assessment of the Blackfeet Indian Reservation, Montana","title":"U.S. Geological Survey and Blackfeet Water Department Hydrologic Assessment of the Blackfeet Indian Reservation, Montana","docAbstract":"The Blackfeet Nation seeks an increased scientific understanding of the water resources within the Blackfeet Indian Reservation of northwestern Montana. Hydrologic information is needed to better inform water-management decisions as the Blackfeet Nation implements the Blackfeet Water Rights Compact, initiates new water-use projects, and improves the Blackfeet Irrigation Project.
The U.S. Geological Survey and the Blackfeet Water Department began cooperating in 2019 to design and implement a hydrologic data-collection program. The program is being implemented in phases that include discrete and continuous discharge measurements of streams and canals, installation, operation of streamgages, groundwater-level monitoring, and database management. Data collected will be used to characterize current hydrologic conditions on the reservation and will act as a baseline for comparison as Blackfeet Nation water projects are implemented.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20233012","collaboration":"Prepared in cooperation with the Blackfeet Water Department","usgsCitation":"Lawlor, S.M., Caldwell, R.R., Bartos, T.T., and Price, B., 2023, U.S. Geological Survey and Blackfeet Water Department Hydrologic Assessment of the Blackfeet Indian Reservation, Montana: U.S. Geological Survey Fact Sheet 2023–3012, 4 p., https://doi.org/10.3133/fs20233012.","productDescription":"Report: 4 p.; Dataset","numberOfPages":"4","onlineOnly":"Y","ipdsId":"IP-141394","costCenters":[{"id":685,"text":"Wyoming-Montana Water Science Center","active":false,"usgs":true}],"links":[{"id":414914,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2023/3012/coverthb2.jpg"},{"id":414918,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2023/3012/images"},{"id":414919,"rank":5,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"},{"id":415102,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/fs20233012/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":499662,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_114650.htm","linkFileType":{"id":5,"text":"html"}},{"id":414917,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2023/3012/fs20233012.XML","text":"Report","linkFileType":{"id":8,"text":"xml"}},{"id":414916,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2023/3012/fs20233012.pdf","text":"Report","size":"2.78 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2023–3012"}],"country":"United States","state":"Montana","otherGeospatial":"Blackfeet Indian Reservation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -113.92382337824559,\n 49.00115786005651\n ],\n [\n -113.92382337824559,\n 47.9492225969058\n ],\n [\n -111.72750252269083,\n 47.9492225969058\n ],\n [\n -111.72750252269083,\n 49.00115786005651\n ],\n [\n -113.92382337824559,\n 49.00115786005651\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
U.S. Geological Survey
3162 Bozeman Avenue
Helena, MT 59601
Managing sustainable harvest of wildlife populations requires regular collection of demographic data and robust estimates of demographic parameters. Estimates can then be used to develop a harvest strategy to guide decision-making. Mallards (Anas platyrhynchos) are an important species in the Atlantic Flyway for many users and they exhibited exponential growth in the eastern United States between the 1970s and 1990s. Since then, estimates of mallard abundance have declined 16%, prompting the Atlantic Flyway Council and United States Fish and Wildlife Service to implement more restrictive hunting regulations and develop a new harvest strategy predicated on an updated population model. Our primary objective was to develop an integrated population model (IPM) for use in an eastern mallard harvest management strategy. We developed an IPM using annual estimates of breeding abundance, 2-season banding and recovery data, and hunter-harvest data from 1998 to 2018. When developing the model, we used novel model selection methods to test various forms of a sub-model for survival including estimating the degree of harvest additivity and any age-specific trends. The top survival sub-model included a negative annual trend on juvenile survival. The IPM posterior estimates for population abundance tracked closely with the observed estimates and estimates of mean annual population growth rate ranged from 0.88 to 1.08. Our population model provided increased precision in abundance estimates compared to survey methods for use in an updated harvest strategy. The IPM posterior estimates of survival rates were relatively stable for adult cohorts, and annual growth rate was positively correlated with the female age ratio, a measure of reproduction. Either or both of those demographic parameters, juvenile survival or reproduction, could be a target for management efforts to address the population decline. The resulting demographic parameters provided information on the equilibrium population size and can be used in an adaptive harvest strategy for mallards in eastern North America.
Poikilothermic animals comprise most species on Earth and are especially sensitive to changes in environmental temperatures. Species conservation in a changing climate relies upon predictions of species responses to future conditions, yet predicting species responses to climate change when temperatures exceed the bounds of observed data is fraught with challenges. We present a physiologically guided abundance (PGA) model that combines observations of species abundance and environmental conditions with laboratory-derived data on the physiological response of poikilotherms to temperature to predict species geographical distributions and abundance in response to climate change. The model incorporates uncertainty in laboratory-derived thermal response curves and provides estimates of thermal habitat suitability and extinction probability based on site-specific conditions. We show that temperature-driven changes in distributions, local extinction, and abundance of cold, cool, and warm-adapted species vary substantially when physiological information is incorporated. Notably, cold-adapted species were predicted by the PGA model to be extirpated in 61% of locations that they currently inhabit, while extirpation was never predicted by a correlative niche model. Failure to account for species-specific physiological constraints could lead to unrealistic predictions under a warming climate, including underestimates of local extirpation for cold-adapted species near the edges of their climate niche space and overoptimistic predictions of warm-adapted species.
","language":"English","publisher":"PNAS","doi":"10.1073/pnas.2214199120","usgsCitation":"Wagner, T., Schliep, E., North, J., Kundel, H., Custer, C., Ruzich, J., and Hansen, G., 2023, Predicting climate change impacts on poikilotherms using physiologically guided species abundance models: Ecology, v. 120, no. 15, e2214199120, 8 p., https://doi.org/10.1073/pnas.2214199120.","productDescription":"e2214199120, 8 p.","ipdsId":"IP-143425","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481069,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1073/pnas.2214199120","text":"Publisher Index Page"},{"id":480740,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"120","issue":"15","noUsgsAuthors":false,"publicationDate":"2023-04-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Wagner, Tyler 0000-0003-1726-016X twagner@usgs.gov","orcid":"https://orcid.org/0000-0003-1726-016X","contributorId":1050,"corporation":false,"usgs":true,"family":"Wagner","given":"Tyler","email":"twagner@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":924257,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schliep, Erin M.","contributorId":349360,"corporation":false,"usgs":false,"family":"Schliep","given":"Erin M.","affiliations":[{"id":7091,"text":"North Carolina State University","active":true,"usgs":false}],"preferred":false,"id":924258,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"North, Joshua S.","contributorId":349361,"corporation":false,"usgs":false,"family":"North","given":"Joshua S.","affiliations":[{"id":6754,"text":"University of Missouri","active":true,"usgs":false}],"preferred":false,"id":924259,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kundel, Holly","contributorId":341087,"corporation":false,"usgs":false,"family":"Kundel","given":"Holly","email":"","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":924431,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Custer, Christopher A.","contributorId":341088,"corporation":false,"usgs":false,"family":"Custer","given":"Christopher A.","affiliations":[{"id":7260,"text":"Pennsylvania State University","active":true,"usgs":false}],"preferred":false,"id":924432,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Ruzich, Jenna K.","contributorId":349365,"corporation":false,"usgs":false,"family":"Ruzich","given":"Jenna K.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":924433,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Hansen, Gretchen J.A.","contributorId":349370,"corporation":false,"usgs":false,"family":"Hansen","given":"Gretchen J.A.","affiliations":[{"id":6626,"text":"University of Minnesota","active":true,"usgs":false}],"preferred":false,"id":924263,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70270840,"text":"70270840 - 2023 - Appendix A: Modeling appendix for the Northwestern and Southwestern pond turtle (Actinemys marmorata , Actinemys pallida )","interactions":[],"lastModifiedDate":"2026-03-16T14:53:10.311939","indexId":"70270840","displayToPublicDate":"2023-04-01T09:35:57","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"title":"Appendix A: Modeling appendix for the Northwestern and Southwestern pond turtle (Actinemys marmorata , Actinemys pallida )","docAbstract":"To predict future status of the northwestern pond turtle (Actinemys marmorata) and southwestern pond turtle (Actinemys pallida) species, we developed a stochastic stage-based matrix population model to simulate future population conditions. We constructed a demographic population viability analysis for each species based on a post-breeding, single sex, stage-based life history diagram elicited from taxa experts and derived from relevant literature. Demographic parameters were based on estimates from published literature and data provided to the U.S. Fish and Wildlife Service (USFWS). Using the most recent observations of turtles, available habitat, local abundances, and current threat conditions, we calculated spatially explicit initial abundances to initialize our stochastic projection. In order to incorporate multiple types of uncertainty (ecological, parametric, temporal), we built three embedded simulation loops within the simulation model. Representing ecological uncertainty, species status was projected into the future using multiple plausible future scenarios based on two representative concentration pathways (RCP 4.5, 8.5) and two shared socioeconomic pathways (SSP 2, 5) to reflect plausible alternative future trajectories of relevant environmental conditions. Parametric uncertainty was
included for survival estimates of all life stages due the inconsistency of estimates across the species’ range. Temporal variability or environmental stochasticity was included in the form of randomized variation from the mean demographic parameter values in each year of the approximately 80-year simulation.
The model output included probability of extinction and estimated abundance through 2100 for each unique Analysis Unit (AU) and for the full geographic range of the species except populations in the state of Washington. The AUs in Washington are conservation dependent and sustained by a head-starting and reintroduction program. Thus, the population dynamics do not match our model for the rest of the range and therefore the Washington AUs were included in this projection modeling effort. There is already pre-existing, detailed PVA for these specific populations (Pramuk et al. 2012, p.41-60), and the Status assessment report can use those results for inference about future status. We discuss the results of Pramuk et al. (2012, p.41-61) alongside our own. Probability of extinction was overall higher for the southwestern pond turtle as compared to the northwestern species and population growth rates were strongly negative for both species (approximately -3% annually for all AUs for all scenarios). This appendix is organized into three primary sections: 1) a description of the life history, the core population dynamics model, and demographic parameters, 2) a description of methods for establishing initial abundances of the populations for the future viability modeling, and 3) a description of the methods for modeling effects of various threats on future demographic rates and the results of future conditions scenarios.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Species status assessment report for Northwestern Pond Turel (Actinemys marmorata) and Southwestern Pond Turtle (Actinemys pallida)","largerWorkSubtype":{"id":1,"text":"Federal Government Series"},"language":"English","publisher":"U.S. Fish and Wildlife Service","usgsCitation":"Gregory, K.M., and McGowan, C.P., 2023, Appendix A: Modeling appendix for the Northwestern and Southwestern pond turtle (Actinemys marmorata , Actinemys pallida ) (version 1.1), 43 p.","productDescription":"43 p.","ipdsId":"IP-146555","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":494872,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.fws.gov/node/5110701"},{"id":501175,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"edition":"version 1.1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gregory, Kaili M.","contributorId":360548,"corporation":false,"usgs":false,"family":"Gregory","given":"Kaili","middleInitial":"M.","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":947202,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McGowan, Conor P. 0000-0002-7330-9581 cmcgowan@usgs.gov","orcid":"https://orcid.org/0000-0002-7330-9581","contributorId":10145,"corporation":false,"usgs":true,"family":"McGowan","given":"Conor","email":"cmcgowan@usgs.gov","middleInitial":"P.","affiliations":[],"preferred":false,"id":947203,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70266579,"text":"70266579 - 2023 - Automated soft pressure sensor array-based sea lamprey detection using machine learning","interactions":[],"lastModifiedDate":"2025-05-09T14:14:32.73205","indexId":"70266579","displayToPublicDate":"2023-04-01T09:10:11","publicationYear":"2023","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":9956,"text":"IEEE Sensors Journal","active":true,"publicationSubtype":{"id":10}},"title":"Automated soft pressure sensor array-based sea lamprey detection using machine learning","docAbstract":"Sea lamprey, a destructive invasive species in the Great Lakes in North America, is among very few fishes that rely on oral suction during migration and spawning. Recently, soft pressure sensors have been proposed to detect the attachment of sea lamprey as part of the monitoring and control effort. However, human decision is still required for the recognition of patterns in the measured signals. In this article, a novel automated soft pressure sensor array-based sea lamprey detection framework is proposed using object detection convolutional neural networks. First, the resistance measurements of the pressure sensor array are converted to mappings of relative change in resistance. These mappings typically show two different types of patterns under lamprey attachment: a high-pressure circular pattern corresponding to the mouth rim compressed against the sensor (“compression” pattern), and a low-pressure blob corresponding to the partial vacuum region of the sucking mouth (“suction” pattern). Three types of object detection algorithms, single-shot detector (SSD), RetinaNet, and YOLOv5s, are applied to the dataset of measurements collected in the presence of sea lamprey attachment, and the comparison of their performance shows that YOLOv5s model achieves the highest mean average precision (mAP) and the fastest inference speed. Furthermore, to improve the accuracy of the prediction model and reduce the false positive (FP) rate due to the sensor’s memory effect, a filter branch with different detection thresholds for the compression and suction patterns, respectively, is added to the original machine-learning algorithm. The trained model is validated and used to automatically detect sea lamprey attachments and locate the suction area on the sensor in real time.
","language":"English","publisher":"IEEE","doi":"10.1109/JSEN.2023.3249625","usgsCitation":"Shi, H., Mei, Y., González-Afanador, I., Chen, C., Miehls, S.M., Holbrook, C., Sepulveda, N., and Tan, X., 2023, Automated soft pressure sensor array-based sea lamprey detection using machine learning: IEEE Sensors Journal, v. 23, p. 7546-7557, https://doi.org/10.1109/JSEN.2023.3249625.","productDescription":"12 p.","startPage":"7546","endPage":"7557","ipdsId":"IP-147136","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":485639,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"23","noUsgsAuthors":false,"publicationDate":"2023-04-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Shi, Hongyang","contributorId":354871,"corporation":false,"usgs":false,"family":"Shi","given":"Hongyang","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936600,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mei, Yu","contributorId":354872,"corporation":false,"usgs":false,"family":"Mei","given":"Yu","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936601,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"González-Afanador, Ian","contributorId":354873,"corporation":false,"usgs":false,"family":"González-Afanador","given":"Ian","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936602,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Chen, Claudia","contributorId":354874,"corporation":false,"usgs":false,"family":"Chen","given":"Claudia","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936603,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Miehls, Scott M. 0000-0002-5546-1854 smiehls@usgs.gov","orcid":"https://orcid.org/0000-0002-5546-1854","contributorId":5007,"corporation":false,"usgs":true,"family":"Miehls","given":"Scott","email":"smiehls@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":936604,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Holbrook, Christopher M. 0000-0001-8203-6856 cholbrook@usgs.gov","orcid":"https://orcid.org/0000-0001-8203-6856","contributorId":139681,"corporation":false,"usgs":true,"family":"Holbrook","given":"Christopher","email":"cholbrook@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":936605,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Sepulveda, Nelson","contributorId":354866,"corporation":false,"usgs":false,"family":"Sepulveda","given":"Nelson","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936606,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Tan, Xiaobo","contributorId":354875,"corporation":false,"usgs":false,"family":"Tan","given":"Xiaobo","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":936607,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70246524,"text":"70246524 - 2023 - Status and trends of pelagic and benthic prey fish populations in Lake Michigan, 2022","interactions":[],"lastModifiedDate":"2024-12-04T22:47:55.278202","indexId":"70246524","displayToPublicDate":"2023-03-31T16:46:20","publicationYear":"2023","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Status and trends of pelagic and benthic prey fish populations in Lake Michigan, 2022","docAbstract":"Fall bottom trawl (fall BT) and lakewide acoustic (AC) surveys are conducted annually to generate indices of pelagic and benthic prey fish densities in Lake Michigan. The fall BT survey has been conducted each fall since 1973 using 12-m trawls at depths ranging from 9 to 110 m at fixed locations distributed across seven transects; this survey estimates densities of seven prey fish species [i.e., Alewife (Alosa pseudoharengus), Bloater (Coregonus hoyi), Rainbow Smelt (Osmerus mordax), Deepwater Sculpin (Myoxocephalus thompsonii), Slimy Sculpin (Cottus cognatus), Round Goby (Neogobius melanostomus), Ninespine Stickleback (Pungitius pungitius)] as well as age-0 Yellow Perch (Perca flavescens) and large (> 350 mm) Burbot (Lota lota). The AC survey has been conducted each late summer/early fall since 2004, and the 2022 survey consisted of 26 transects [570 km total (354 miles)] covering bottom depths ranging from 5 to 255 m and 37 midwater trawl tows above bottom depths ranging 5 to 232 m; this survey estimates densities of three prey fish species (i.e., Alewife, Bloater, and Rainbow Smelt). The data generated from these surveys are used to estimate various population parameters that are, in turn, used by state and tribal agencies in managing Lake Michigan fish stocks. In spring of 2022, an additional spring bottom trawl survey (spring BT) was implemented across six of the transects sampled in the fall and sites ranged in depth from 9 to 236 m. The goal of the spring BT was to explore seasonal differences in biomass density and distributions of key prey species, mostly notably Alewife.
Total prey fish biomass density from the spring BT was 2.1 kg/ha. For the AC survey, total biomass density of prey fish equaled 6.2 kg/ha, 37% higher than the long-term average (2004-2021) of 4.5 kg/ha and 0.43 kg/ha higher than the 2021 estimate. For the fall BT, total biomass density of prey fish equaled 8.7 kg/ha, the highest value since 2013 and 21% higher than average value from 20042021 (6.8 kg/ha). The 2022 fall BT biomass density was still well below the average over the entirety of the time series (1973-2021; 34.3 kg/ha). Over the period both surveys have been conducted (2004-2021), total biomass density has trended downward in the fall BT (despite a high 2022 estimate) and remained relatively stable in the AC survey.
Bloater was the dominant species (by biomass) among prey fishes in both the spring and fall BT, while the AC survey reported co-dominance of Bloater and Alewife. Mean biomass of yearling and older (YAO) Alewife was 0.38 kg/ha in the spring BT, 3.0 kg/ha in the AC survey, and 0.10 in the fall BT. Alewife were aggregated in deepwater habitats in the spring of 2022 (> 110 m). Since 2014, catchability of YAO Alewives for the fall BT has been substantially lower than the AC survey. Results of the 2022 spring BT do not suggest that catchability is substantially higher in the spring than the fall.
Comparing the acoustic estimate to previous years, YAO Alewife biomass was 40% higher than the average from 2004-2021. An age-7 fish was recorded for the first time since 2009. Despite the rare catches of older fish, the Alewife age distribution still appears truncated, with age-1 fish as the most represented age class in all three surveys. Numeric density of age-0 Alewife from the AC survey was 7 fish/ha in 2022, which is the third lowest in the time series and well below the longterm mean of 452 fish/ha. Biomass density of large (≥120 mm) Bloater was 2.7 kg/ha in the AC survey and 4.4 kg/ha in the fall BT - each at least an order of magnitude lower than what was estimated by the fall BT between 1981 and 1998. Following a record high year in 2021 (1,037 fish/ha), the numeric density of small (<120 mm) Bloater was only 15 fish/ha in the AC survey.
Meanwhile, small Bloater density estimated in the fall BT was 261 fish/ha, the highest value since 1990 and likely partially reflective of a large 2021 year-class. Biomass density of large Rainbow Smelt (≥90 mm) was 0.29 kg/ha in the AC survey and 0.12 kg/ha in the fall BT survey, continuing the trend of low Rainbow Smelt biomass that has been observed since 2001. Numeric density of small (<90 mm) Rainbow Smelt was 21 fish/ha in the AC survey and 2.7 fish/ha in the fall BT, indicating a weak year-class. All four prey fish species sampled only by the fall BT indicated below average biomass densities. Deepwater Sculpin biomass density was estimated at 0.41 kg/ha, which makes 12 of the past 13 years when biomass was <1 kg/ha. Slimy Sculpin was estimated at 0.10 kg/ha, the highest estimate since 2016 but still only 25% of the long-term average. Round Goby was estimated at 1.3 kg/ha, above the average biomass of 0.82 kg/ha since 2008 but similar to intermittent high values observed throughout the dataset. Ninespine Stickleback density was 1.5 fish/ha. Burbot biomass remained near record low levels, and no age-0 Yellow Perch were caught, indicating a weak Yellow Perch year-class in 2022.
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