{"pageNumber":"92","pageRowStart":"2275","pageSize":"25","recordCount":40777,"records":[{"id":70252011,"text":"70252011 - 2024 - Complex landslide patterns explained by local intra-unit variability of stratigraphy and structure: Case study in the Tyee Formation, Oregon, USA","interactions":[],"lastModifiedDate":"2024-03-11T12:18:44.85142","indexId":"70252011","displayToPublicDate":"2024-01-06T07:15:17","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1517,"text":"Engineering Geology","active":true,"publicationSubtype":{"id":10}},"title":"Complex landslide patterns explained by local intra-unit variability of stratigraphy and structure: Case study in the Tyee Formation, Oregon, USA","docAbstract":"<p>Lithology and geologic structure are important controls on landslide susceptibility and are incorporated into many regional landslide hazard models. Typically, metrics for mapped geologic units are used as model input variables and a single set of values for material strength are assumed, regardless of spatial heterogeneities that may exist within a map unit. Here we describe how differences in bedding thickness, grain size, inferred uniaxial compressive strength, and bedding dip control the inherent susceptibility of slopes to deep-seated failure within a single mapped geologic unit - the Tyee Formation of Oregon, USA. The Tyee, which covers over 15,000 km2 and underlies much of the Oregon Coast Range, comprises gently folded alternating beds of sandstone and siltstone deposited as turbidites, forming a 2-km thick Eocene submarine fan which has been uplifted and exhumed through the Cenozoic. Deep-seated landslides are widespread in the Tyee, but form a complex spatial pattern such that landslide density ranges from 0 to 24% of the total landscape area. These slides are often extensive and sufficiently deep to reduce local hillslope gradients, resulting in a strong negative correlation between landslide density and mean local slope. Mean annual precipitation and predicted strong ground motions from Cascadia earthquake scenarios also fail to explain the spatial distribution of deep-seated landslides. Consequently, landslide stability models, which are strongly influenced by landscape slope, pore-water pressure, and seismic acceleration, yield landslide susceptibility maps which are broadly anti-correlated with mapped deep-seated landslide density. Through a multivariable linear regression model, we show that much of the variance in deep-seated landslide density can be explained by variability of intra-unit stratigraphic and structural characteristics, which we measure at 128 sites across two study areas totaling ∼3000 km2. Our results suggest bedding dip is only weakly correlated to landslide density, but strongly influences landslide failure style. Subtle increases in bedding dip, even in the gently folded Tyee Formation, result in a substantially higher likelihood of a landslide being cataclinal, or parallel to bedding. Overall, we find a slight majority of landslides fail within these cataclinal slopes, and that these landslides tend to be larger than non-cataclinal landslides. We also show that the lithological and structural properties that influence landslide susceptibility are distinct for these two populations of landslides. Our results demonstrate how localized, intra-unit, geologic variability can exert strong control on landslide susceptibility and failure style. This suggests that in some locations, landslide hazard models could be significantly improved by incorporating detailed, spatially variable, geologic properties rather than relying solely on generalized geologic map units.</p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.enggeo.2023.107387","usgsCitation":"LaHusen, S.R., and Grant, A.R., 2024, Complex landslide patterns explained by local intra-unit variability of stratigraphy and structure: Case study in the Tyee Formation, Oregon, USA: Engineering Geology, v. 329, 107387, 14 p., https://doi.org/10.1016/j.enggeo.2023.107387.","productDescription":"107387, 14 p.","ipdsId":"IP-146959","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":440788,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.enggeo.2023.107387","text":"Publisher Index Page"},{"id":426489,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Tyee Formation","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -125.10474453625554,\n              42.47204971687748\n            ],\n            [\n              -121.72095547375605,\n              42.47204971687748\n            ],\n            [\n              -121.72095547375605,\n              45.35210028381104\n            ],\n            [\n              -125.10474453625554,\n              45.35210028381104\n            ],\n            [\n              -125.10474453625554,\n              42.47204971687748\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"329","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"LaHusen, Sean Richard 0000-0003-4246-4439","orcid":"https://orcid.org/0000-0003-4246-4439","contributorId":294677,"corporation":false,"usgs":true,"family":"LaHusen","given":"Sean","email":"","middleInitial":"Richard","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":896262,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grant, Alex R. 0000-0002-5096-4305","orcid":"https://orcid.org/0000-0002-5096-4305","contributorId":219066,"corporation":false,"usgs":true,"family":"Grant","given":"Alex","middleInitial":"R.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":896263,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70250949,"text":"70250949 - 2024 - Environmental and geographical factors influence the occurrence and abundance of the southern house mosquito, Culex quinquefasciatus, in Hawai‘i","interactions":[],"lastModifiedDate":"2024-01-13T15:15:09.210779","indexId":"70250949","displayToPublicDate":"2024-01-05T09:11:23","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3358,"text":"Scientific Reports","active":true,"publicationSubtype":{"id":10}},"title":"Environmental and geographical factors influence the occurrence and abundance of the southern house mosquito, Culex quinquefasciatus, in Hawai‘i","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>Hawaiian honeycreepers, a group of endemic Hawaiian forest birds, are being threatened by avian malaria, a non-native disease that is driving honeycreepers populations to extinction. Avian malaria is caused by the parasite<span>&nbsp;</span><i>Plasmodium relictum</i>, which is transmitted by the invasive mosquito<span>&nbsp;</span><i>Culex quinquefasciatus</i>. Environmental and geographical factors play an important role in shaping mosquito-borne disease transmission dynamics through their influence on the distribution and abundance of mosquitoes. We assessed the effects of environmental (temperature, precipitation), geographic (site, elevation, distance to anthropogenic features), and trap type (CDC light trap, CDC gravid trap) factors on mosquito occurrence and abundance. Occurrence was analyzed using classification and regression tree models (CART) and generalized linear models (GLM); abundance (count data) was analyzed using generalized linear mixed models (GLMMs). Models predicted highest mosquito occurrence at mid-elevation sites and between July and November. Occurrence increased with temperature and precipitation up to 580&nbsp;mm. For abundance, the best model was a zero-inflated negative-binomial model that indicated higher abundance of mosquitoes at mid-elevation sites and peak abundance between August and October. Estimation of occurrence and abundance as well as understanding the factors that influence them are key for mosquito control, which may reduce the risk of forest bird extinction.</p></div></div>","language":"English","publisher":"Nature","doi":"10.1038/s41598-023-49793-9","usgsCitation":"Villena, O., McClure, K.M., Camp, R.J., Lapointe, D., Atkinson, C., Sofaer, H., and Fortini, L., 2024, Environmental and geographical factors influence the occurrence and abundance of the southern house mosquito, Culex quinquefasciatus, in Hawai‘i: Scientific Reports, v. 14, 604, 14 p., https://doi.org/10.1038/s41598-023-49793-9.","productDescription":"604, 14 p.","ipdsId":"IP-150482","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"links":[{"id":440790,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-023-49793-9","text":"Publisher Index Page"},{"id":435064,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P95LVJIC","text":"USGS data release","linkHelpText":"Island of Hawaii bird, mosquito, and avian malaria infection data 2001-2004"},{"id":424420,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -154.5318706383728,\n              19.684618403415485\n            ],\n            [\n              -155.21302298212288,\n              19.684618403415485\n            ],\n            [\n              -155.21302298212288,\n              19.25994400883974\n            ],\n            [\n              -154.5318706383728,\n              19.25994400883974\n            ],\n            [\n              -154.5318706383728,\n              19.684618403415485\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"14","noUsgsAuthors":false,"publicationDate":"2024-01-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Villena, Oswaldo","contributorId":333277,"corporation":false,"usgs":false,"family":"Villena","given":"Oswaldo","email":"","affiliations":[{"id":13341,"text":"Hawai‘i Cooperative Studies Unit, University of Hawai‘i at Hilo","active":true,"usgs":false}],"preferred":false,"id":892347,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McClure, Katherine Maria 0000-0001-8595-7677","orcid":"https://orcid.org/0000-0001-8595-7677","contributorId":332279,"corporation":false,"usgs":true,"family":"McClure","given":"Katherine","email":"","middleInitial":"Maria","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":892348,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Camp, Richard J. 0000-0001-7008-923X rick_camp@usgs.gov","orcid":"https://orcid.org/0000-0001-7008-923X","contributorId":189964,"corporation":false,"usgs":true,"family":"Camp","given":"Richard","email":"rick_camp@usgs.gov","middleInitial":"J.","affiliations":[{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":892349,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"LaPointe, Dennis A. 0000-0002-6323-263X dlapointe@usgs.gov","orcid":"https://orcid.org/0000-0002-6323-263X","contributorId":150365,"corporation":false,"usgs":true,"family":"LaPointe","given":"Dennis","email":"dlapointe@usgs.gov","middleInitial":"A.","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":892350,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Atkinson, Carter T. 0000-0002-4232-5335","orcid":"https://orcid.org/0000-0002-4232-5335","contributorId":302619,"corporation":false,"usgs":true,"family":"Atkinson","given":"Carter T.","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":892351,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sofaer, Helen 0000-0002-9450-5223","orcid":"https://orcid.org/0000-0002-9450-5223","contributorId":216681,"corporation":false,"usgs":true,"family":"Sofaer","given":"Helen","email":"","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":892352,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Fortini, Lucas Berio 0000-0002-5781-7295","orcid":"https://orcid.org/0000-0002-5781-7295","contributorId":236984,"corporation":false,"usgs":true,"family":"Fortini","given":"Lucas Berio","affiliations":[{"id":521,"text":"Pacific Island Ecosystems Research Center","active":false,"usgs":true}],"preferred":true,"id":892353,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70251132,"text":"70251132 - 2024 - Heterogeneous multi-stage accretionary orogenesis — Evidence from the Gunnison block in the Yavapai Province, southwest USA","interactions":[],"lastModifiedDate":"2024-01-24T13:12:49.893969","indexId":"70251132","displayToPublicDate":"2024-01-05T07:09:56","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3112,"text":"Precambrian Research","active":true,"publicationSubtype":{"id":10}},"title":"Heterogeneous multi-stage accretionary orogenesis — Evidence from the Gunnison block in the Yavapai Province, southwest USA","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif text-s\"><div id=\"ab010\" class=\"abstract author\"><div id=\"as010\"><p id=\"sp0010\">Proterozoic rocks exposed in the southwestern U.S.A. represent one of the best examples of crustal growth by arc-related magmatism and accretionary orogenesis. Within the Southwest the 1.8–1.7&nbsp;Ga Yavapai Province is widely regarded as a classic example of juvenile arc crust, however 1.8–2.5&nbsp;Ga inherited zircon and Nd and Hf model ages have been recognized near Gunnison in central Colorado. These data have led to questions regarding the extent and nature of pre-1.8&nbsp;Ga crustal material and the genesis of the Yavapai Province. We present evidence for a geochemically distinct, spatially restricted crustal block underlain by pre-1.8&nbsp;Ga crust material (referred to here as the Gunnison block) in central to western Colorado within the Yavapai Province. The Gunnison block is characterized by 1.8–1.9 and 2.4–2.6&nbsp;Ga inherited zircon, Pb isotopic systematics (μ&nbsp;=&nbsp;9.8&nbsp;±&nbsp;0.1, κ&nbsp;=&nbsp;3.7&nbsp;±&nbsp;0.1) elevated relative to 1.8&nbsp;Ga depleted mantle values, 1.8–2.5&nbsp;Ga Nd and Hf model ages, and a distinct pressure-temperature-time history. The geochemical data are consistent with mixing between juvenile 1.8&nbsp;Ga and pre-1.8&nbsp;Ga sources. The older crustal component is most similar to the isotopically enriched Mojave Province of eastern California and western Arizona, suggesting greater similarities between these provinces than previously recognized. Monazite and xenotime petrochronology indicate ca. 1.75–1.74, 1.72–1.69, 1.67, and 1.47–1.38&nbsp;Ga tectono-metamorphic events. These data suggest that the Gunnison block accreted to other components of the Yavapai Province outboard of Laurentia at 1.75–1.74&nbsp;Ga. The composite Yavapai Province was accreted to the margin of Laurentia during the 1.72–1.69&nbsp;Ga Yavapai orogeny. Later overprinting is associated with the ∼1.68–1.60&nbsp;Ga Mazatzal and ∼1.47–1.37&nbsp;Ga Picuris orogenies. Identification of distinct crustal terranes within the Yavapai Province supports models involving multiple arcs and back-arcs that were progressively assembled prior to their accretion to Laurentia, perhaps akin to the present-day Banda Sea in Indonesia.</p></div></div></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.precamres.2023.107256","usgsCitation":"Hillenbrand, I.W., Gilmer, A.K., Williams, M.L., Karlstrom, K.E., Souders, A., Vazquez, J.A., and Premo, W.R., 2024, Heterogeneous multi-stage accretionary orogenesis — Evidence from the Gunnison block in the Yavapai Province, southwest USA: Precambrian Research, v. 401, 107256, 22 p., https://doi.org/10.1016/j.precamres.2023.107256.","productDescription":"107256, 22 p.","ipdsId":"IP-157072","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":467039,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.precamres.2023.107256","text":"Publisher Index Page"},{"id":424854,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Yavapai Province","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -116.1594154127937,\n              43.73112981678608\n            ],\n            [\n              -116.1594154127937,\n              30.476743970877664\n            ],\n            [\n              -101.04222791279368,\n              30.476743970877664\n            ],\n            [\n              -101.04222791279368,\n              43.73112981678608\n            ],\n            [\n              -116.1594154127937,\n              43.73112981678608\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"401","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Hillenbrand, Ian William 0000-0003-2801-3674","orcid":"https://orcid.org/0000-0003-2801-3674","contributorId":299032,"corporation":false,"usgs":true,"family":"Hillenbrand","given":"Ian","email":"","middleInitial":"William","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":893219,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gilmer, Amy K. 0000-0001-5038-8136","orcid":"https://orcid.org/0000-0001-5038-8136","contributorId":218307,"corporation":false,"usgs":true,"family":"Gilmer","given":"Amy","email":"","middleInitial":"K.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":893220,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Williams, Michael L.","contributorId":215495,"corporation":false,"usgs":false,"family":"Williams","given":"Michael","email":"","middleInitial":"L.","affiliations":[{"id":37201,"text":"UMass Amherst","active":true,"usgs":false}],"preferred":false,"id":893221,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Karlstrom, Karl E.","contributorId":228844,"corporation":false,"usgs":false,"family":"Karlstrom","given":"Karl","email":"","middleInitial":"E.","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":893222,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Souders, Amanda 0000-0002-1367-8924","orcid":"https://orcid.org/0000-0002-1367-8924","contributorId":296423,"corporation":false,"usgs":true,"family":"Souders","given":"Amanda","email":"","affiliations":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":893223,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Vazquez, Jorge A. 0000-0003-2754-0456 jvazquez@usgs.gov","orcid":"https://orcid.org/0000-0003-2754-0456","contributorId":4458,"corporation":false,"usgs":true,"family":"Vazquez","given":"Jorge","email":"jvazquez@usgs.gov","middleInitial":"A.","affiliations":[{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":5056,"text":"Office of the AD Energy and Minerals, and Environmental Health","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":501,"text":"Office of Science Quality and Integrity","active":true,"usgs":true}],"preferred":true,"id":893224,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Premo, Wayne R. 0000-0001-9904-4801 wpremo@usgs.gov","orcid":"https://orcid.org/0000-0001-9904-4801","contributorId":1697,"corporation":false,"usgs":true,"family":"Premo","given":"Wayne","email":"wpremo@usgs.gov","middleInitial":"R.","affiliations":[],"preferred":true,"id":893225,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70256561,"text":"70256561 - 2024 - Landscape-scale population trends in the occurrence and abundance of wildlife populations using long term camera-trapping data","interactions":[],"lastModifiedDate":"2024-08-19T12:01:52.94453","indexId":"70256561","displayToPublicDate":"2024-01-05T06:50:22","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Landscape-scale population trends in the occurrence and abundance of wildlife populations using long term camera-trapping data","docAbstract":"<div id=\"preview-section-abstract\"><div id=\"abstracts\" class=\"Abstracts u-font-serif\"><div id=\"ab0005\" class=\"abstract author\" lang=\"en\"><div id=\"as0005\"><p id=\"sp0020\"><span>Accurate estimation and monitoring of wildlife population trends is foundational to evidence-based conservation. Here, we use hierarchical modelling to estimate population trends for six species of management interest (coyotes;&nbsp;<a class=\"topic-link\" title=\"Learn more about red foxes from ScienceDirect's AI-generated Topic Pages\" href=\"https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/vulpes-vulpes\" data-mce-href=\"https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/vulpes-vulpes\">red foxes</a>, white-tailed&nbsp;<a class=\"topic-link\" title=\"Learn more about deer from ScienceDirect's AI-generated Topic Pages\" href=\"https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/cervidae\" data-mce-href=\"https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/cervidae\">deer</a>, gray foxes; eastern&nbsp;<a class=\"topic-link\" title=\"Learn more about wild turkey from ScienceDirect's AI-generated Topic Pages\" href=\"https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/meleagris-gallopavo\" data-mce-href=\"https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/meleagris-gallopavo\">wild turkey</a>, and bobcats) while accounting for observation error from a long-term&nbsp;<a class=\"topic-link\" title=\"Learn more about camera trap from ScienceDirect's AI-generated Topic Pages\" href=\"https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/camera-trap\" data-mce-href=\"https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/camera-trap\">camera trap</a>&nbsp;survey conducted across the State of New York. We were able to detect population level trends in occurrence and abundance and produce spatially explicit predictions for all six species using a combination of single-species occupancy models and Royle-Nichols models. Coyote (mean λ&nbsp;=&nbsp;1.22, 95&nbsp;% CI&nbsp;=&nbsp;0.85–1.82) and red fox (mean λ&nbsp;=&nbsp;1.17, 95&nbsp;% CI&nbsp;=&nbsp;0.95–1.46) populations were widely distributed with stable populations across the sampling period from 2014 to 2021. White-tailed&nbsp;<a class=\"topic-link\" title=\"Learn more about deer from ScienceDirect's AI-generated Topic Pages\" href=\"https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/cervidae\" data-mce-href=\"https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/cervidae\">deer</a>&nbsp;populations were highly abundant and displayed an increasing population trend (mean λ&nbsp;=&nbsp;1.85, 95&nbsp;% CI&nbsp;=&nbsp;1.54–2.10). Eastern&nbsp;<a class=\"topic-link\" title=\"Learn more about wild turkey from ScienceDirect's AI-generated Topic Pages\" href=\"https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/meleagris-gallopavo\" data-mce-href=\"https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/meleagris-gallopavo\">wild turkey</a>&nbsp;occupancy remained low across the state despite displaying a slight increase in occupancy over the sampling period (mean&nbsp;</span><i>ψ</i>&nbsp;=&nbsp;0.16, 95&nbsp;% CI&nbsp;=&nbsp;0.07–0.25). Gray fox occupancy was also low (mean<span>&nbsp;</span><i>ψ</i>&nbsp;=&nbsp;0.22, 95&nbsp;% CI&nbsp;=&nbsp;0.12–0.29), consistent with growing concerns over the species across North America. Despite recent recoveries elsewhere, bobcat populations in New York State displayed very low occupancy (mean<span>&nbsp;</span><i>ψ</i>&nbsp;=&nbsp;0.07, 95&nbsp;% CI&nbsp;=&nbsp;0.02–0.12), highlighting the necessity of monitoring to inform conservation action. We provide empirically supported management implications for each species and demonstrate the efficacy of long-term camera trapping to provide robust evidence on population trends while accounting for imperfect detections, over scales meaningful to species management and conservation.</p></div></div></div></div><div id=\"preview-section-introduction\"><br></div>","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2023.110398","usgsCitation":"Twining, J.P., Kramer, D., Perkins, K.A., and Fuller, A.K., 2024, Landscape-scale population trends in the occurrence and abundance of wildlife populations using long term camera-trapping data: Biological Conservation, v. 290, 110398, https://doi.org/10.1016/j.biocon.2023.110398.","productDescription":"110398","ipdsId":"IP-151775","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":432880,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"290","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Twining, Joshua P.","contributorId":341149,"corporation":false,"usgs":false,"family":"Twining","given":"Joshua","email":"","middleInitial":"P.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":908002,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kramer, David","contributorId":341150,"corporation":false,"usgs":false,"family":"Kramer","given":"David","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":908003,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Perkins, Kelly A.","contributorId":341151,"corporation":false,"usgs":false,"family":"Perkins","given":"Kelly","email":"","middleInitial":"A.","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":908004,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fuller, Angela K. 0000-0002-9247-7468 afuller@usgs.gov","orcid":"https://orcid.org/0000-0002-9247-7468","contributorId":3984,"corporation":false,"usgs":true,"family":"Fuller","given":"Angela","email":"afuller@usgs.gov","middleInitial":"K.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":908005,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70250514,"text":"70250514 - 2024 - The importance of nodal plane orientation diversity for earthquake focal mechanism stress inversions","interactions":[],"lastModifiedDate":"2024-08-26T14:13:59.855005","indexId":"70250514","displayToPublicDate":"2024-01-05T06:35:47","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5011,"text":"Geological Society of London Special Publications","active":true,"publicationSubtype":{"id":10}},"title":"The importance of nodal plane orientation diversity for earthquake focal mechanism stress inversions","docAbstract":"<div>Inversions of earthquake focal mechanisms are among the most accessible and reliable methods for determining crustal stress. However, the use of this method varies widely, and assumptions that underpin it are often violated, potentially compromising stress estimates. We investigate the consequences of violating the little-studied assumption that the focal mechanisms have diverse orientations. Our approach is to employ data-informed synthetic mechanisms, with nodal plane orientations defined by recent earthquake lineaments in the Midland Basin, western Texas, and rakes consistent with slip in the mapped stress field. Using both the traditional stress inversion method that assumes constant shear stress magnitudes on the causative faults as well as a recently published variable shear stress method, we show that low fault plane diversity can cause maximum horizontal stress (<i>S</i><sub>Hmax</sub>) orientation and relative principal stress magnitude (faulting regime) estimates to differ markedly from the true values. This problem is compounded for catalogs with even modest amounts of noise (≤15°) or few (e.g., 20) mechanisms. Significantly, traditional approaches for quantifying uncertainty such as the bootstrap can severely underestimate the true uncertainty under these circumstances. To remedy this, we provide simple tools to quantify nodal plane orientation diversity and stress inversion reliability.</div>","language":"English","publisher":"Geological Society of London","doi":"10.1144/SP546-2023-63","usgsCitation":"Lundstern, J., Beauce, E., and Teran, O.J., 2024, The importance of nodal plane orientation diversity for earthquake focal mechanism stress inversions: Geological Society of London Special Publications, v. 546, p. 93-118, https://doi.org/10.1144/SP546-2023-63.","productDescription":"26 p.","startPage":"93","endPage":"118","ipdsId":"IP-151936","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":467040,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1144/sp546-2023-63","text":"Publisher Index Page"},{"id":423571,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"546","noUsgsAuthors":false,"publicationDate":"2024-01-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Lundstern, Jens-Erik 0000-0003-0000-8013","orcid":"https://orcid.org/0000-0003-0000-8013","contributorId":264189,"corporation":false,"usgs":true,"family":"Lundstern","given":"Jens-Erik","email":"","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":890216,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Beauce, Eric 0000-0003-3138-9082","orcid":"https://orcid.org/0000-0003-3138-9082","contributorId":332461,"corporation":false,"usgs":false,"family":"Beauce","given":"Eric","email":"","affiliations":[{"id":28041,"text":"Lamont-Doherty Earth Observatory, Columbia University","active":true,"usgs":false}],"preferred":false,"id":890217,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Teran, Orlando J. 0000-0003-1409-1508","orcid":"https://orcid.org/0000-0003-1409-1508","contributorId":332462,"corporation":false,"usgs":false,"family":"Teran","given":"Orlando","email":"","middleInitial":"J.","affiliations":[{"id":79470,"text":"Ovintiv","active":true,"usgs":false}],"preferred":false,"id":890218,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}}
,{"id":70251052,"text":"70251052 - 2024 - Using local monitoring results to inform the Chesapeake Bay Program’s Watershed Model","interactions":[],"lastModifiedDate":"2024-01-19T15:15:53.833192","indexId":"70251052","displayToPublicDate":"2024-01-04T09:15:25","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"seriesTitle":{"id":17129,"text":"STAC Workshop Report","active":true,"publicationSubtype":{"id":3}},"seriesNumber":"24-002","title":"Using local monitoring results to inform the Chesapeake Bay Program’s Watershed Model","docAbstract":"The Chesapeake Bay Program’s Watershed Model (CBWM) has been used as an accounting tool for the Chesapeake Bay Total Maximum Daily Load (TMDL).  However, some of the fundamental parameters that underpin the watershed model may not represent local watershed characteristics at all scales. Significant investments have been made by state and local governments, and other local stakeholders, who are interested in validating loads and progress in implementing measures to achieve the pollutant reductions called for in the TMDL through local monitoring data. For the purposes of this STAC workshop, local monitoring is considered any relevant data collected by a local, regional, state, or federal organization that has not been used previously in the development, calibration, or validation of the CBWM. Some of these local monitoring efforts have been collecting data over the past 5-10 years, with some datasets extending back over more than two decades. However, the data and the CBWM are often not directly comparable due to differences in temporal and spatial scales or because the water quality parameters being monitored are not those estimated by the model. Therefore, a Scientific and Technical Advisory Committee (STAC) workshop was convened to bring together Chesapeake Bay Program (CBP) modelers, local and state government stakeholders, and scientists who are monitoring and analyzing local water quality data to recommend ways in which local monitoring data can be used to inform the CBWM, identify gaps between modeled and monitored data, and validate model predictions at the local scale.\n\nThe workshop, “Using Local Monitoring Results to Inform the Chesapeake Bay Program’s Watershed Model”, was held in March 2023 to provide insight on the scope of local water quality monitoring efforts within and outside of the Bay watershed that could be used to inform the CBWM.  Scientists and managers developed recommendations that could be used by modelers for either calibration or knowledge generation to inform the Phase 7 version of the CBWM currently under development for a 2027 decision by the CBP, recommendations for how local monitoring efforts could be designed or altered to better inform the CBWM, and recommendations for how monitored trends could be used in management. The preliminary presentations for the workshop provided essential background information on the CBWM and data used to parameterize it. This information was the foundation for discussions on existing data gaps, the importance of current local monitoring networks, and best practices for developing future monitoring networks. More information on this STAC-funded effort including workshop presentation slides and recordings can be accessed on the workshop webpage. \n\nConfidence in the loading estimates of the CBWM is critical because of its role as the accounting mechanism for measuring progress toward the Bay TMDL’s nutrient and sediment reduction goals. Those who are being asked or required to pay for these reductions, from state and local government managers to farmers, property owners and developers, must have confidence in the scientific validity of the CBWM’s loading estimates or trust in the restoration effort will dissipate. Toward that end, several local entities have invested in extensive urban, suburban, and agricultural monitoring programs to characterize nutrient and sediment loading (among other water quality parameters) at a relatively fine scale (from a few acres to 5 square miles). Monitoring networks outside of the Bay watershed were also included as their relevance and similarities to Bay watershed landscapes, hydrology, and climate conditions can help build the body of knowledge necessary for better parameterization of the CBWM.\nLocal monitoring results could be analyzed for loads and trends for calibration of Phase 7, comparison against trends, informing the structure and parameterization of the model, and potentially in policy evaluation. The effectiveness of management practices at the small watershed scale is a primary question of watershed managers that could be addressed by local monitoring, but to do so study design and statistical techniques may need to be altered if these datasets are intended to inform parameterization of the Bay modeling tools.  The partnership would benefit from the redesign of some existing monitoring programs so that they are hypothesis-driven, with fully described inputs, outputs, and practices.  New statistical tools could be applied to evaluate the relative importance of various drivers affecting water quality and influenced by hydrogeologic setting and watershed condition.","language":"English","publisher":"Chesapeake Bay Program STAC (Scientific and Technical Advisory Committee)","usgsCitation":"Berger, K., Filippino, K.C., Shenk, G.W., Goulet, N., Lookenbill, M., Moyer, D.L., Noe, G.E., Porter, A.J., Shallenberger, J., Thomas, B., and Yactayo, G., 2024, Using local monitoring results to inform the Chesapeake Bay Program’s Watershed Model: STAC Workshop Report 24-002, 35 p.","productDescription":"35 p.","ipdsId":"IP-160274","costCenters":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":424622,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":424607,"rank":1,"type":{"id":15,"text":"Index 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Governments","active":true,"usgs":false}],"preferred":false,"id":892892,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Filippino, Katherine C.","contributorId":333489,"corporation":false,"usgs":false,"family":"Filippino","given":"Katherine","email":"","middleInitial":"C.","affiliations":[{"id":79898,"text":"Hampton Roads Planning District Commission","active":true,"usgs":false}],"preferred":false,"id":892893,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shenk, Gary W. 0000-0001-6451-2513","orcid":"https://orcid.org/0000-0001-6451-2513","contributorId":225440,"corporation":false,"usgs":true,"family":"Shenk","given":"Gary","email":"","middleInitial":"W.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":892894,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Goulet, Normand","contributorId":333490,"corporation":false,"usgs":false,"family":"Goulet","given":"Normand","email":"","affiliations":[{"id":79899,"text":"Norther Virginia Regional Planning District Commission","active":true,"usgs":false}],"preferred":false,"id":892895,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lookenbill, Michael 0000-0001-5857-8276","orcid":"https://orcid.org/0000-0001-5857-8276","contributorId":236910,"corporation":false,"usgs":false,"family":"Lookenbill","given":"Michael","email":"","affiliations":[{"id":17703,"text":"Pennsylvania Department of Environmental Protection","active":true,"usgs":false}],"preferred":false,"id":892896,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Moyer, Douglas L. 0000-0001-6330-478X dlmoyer@usgs.gov","orcid":"https://orcid.org/0000-0001-6330-478X","contributorId":174389,"corporation":false,"usgs":true,"family":"Moyer","given":"Douglas","email":"dlmoyer@usgs.gov","middleInitial":"L.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":892897,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Noe, Gregory E. 0000-0002-6661-2646 gnoe@usgs.gov","orcid":"https://orcid.org/0000-0002-6661-2646","contributorId":139100,"corporation":false,"usgs":true,"family":"Noe","given":"Gregory","email":"gnoe@usgs.gov","middleInitial":"E.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":892898,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Porter, Aaron J. 0000-0002-0781-3309","orcid":"https://orcid.org/0000-0002-0781-3309","contributorId":239980,"corporation":false,"usgs":true,"family":"Porter","given":"Aaron","email":"","middleInitial":"J.","affiliations":[{"id":37759,"text":"VA/WV Water Science Center","active":true,"usgs":true}],"preferred":true,"id":892899,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Shallenberger, James","contributorId":333491,"corporation":false,"usgs":false,"family":"Shallenberger","given":"James","email":"","affiliations":[{"id":79900,"text":"Susquehanna River Basin Commission","active":true,"usgs":false}],"preferred":false,"id":892900,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Thomas, Bryant","contributorId":333492,"corporation":false,"usgs":false,"family":"Thomas","given":"Bryant","email":"","affiliations":[{"id":39875,"text":"Virginia Department of Environmental Quality","active":true,"usgs":false}],"preferred":false,"id":892901,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Yactayo, Guido","contributorId":333493,"corporation":false,"usgs":false,"family":"Yactayo","given":"Guido","email":"","affiliations":[{"id":27050,"text":"Maryland Department of the Environment","active":true,"usgs":false}],"preferred":false,"id":892902,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}}
,{"id":70255769,"text":"70255769 - 2024 - Contrasting demographic responses under future climate for two populations of a montane amphibian","interactions":[],"lastModifiedDate":"2024-07-03T12:04:54.242749","indexId":"70255769","displayToPublicDate":"2024-01-04T07:03:42","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":12584,"text":"Climate Change Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Contrasting demographic responses under future climate for two populations of a montane amphibian","docAbstract":"<div id=\"abstracts\" class=\"Abstracts u-font-serif text-s\"><div id=\"abs0002\" class=\"abstract author\"><div id=\"abss0002\"><p id=\"spara008\">For species with complex life histories, climate change can have contrasting effects for different life stages within locally adapted populations and may result in responses counter to general climate change predictions. Using data from two, 14-year demographic studies for a North American montane amphibian, Cascades frog (<i>Rana cascadae</i>), we quantified how aspects of current climate influenced annual survival of larvae and adult stages and modeled the stochastic population growth rate (λ<sub>s</sub>) of each population for current (1980–2006) and future periods (2080s). Climate drivers of survival for the populations were similar for larvae (i.e., decreases in precipitation lead to pond drying and mortality), but diverged for terrestrial stages where decreases in winter length and summer precipitation had opposite effects. By the 2080s, we predict one population will be in sharp decline (λ<sub>s</sub>&nbsp;=&nbsp;0.90), while the other population will remain nearly stable (λ<sub>s</sub>&nbsp;=&nbsp;0.99) in the absence of other stressors, such as mortality due to disease. Our case study demonstrates a result counter to many climate envelope predictions in that stage-specific responses to local climate and hydrology result in a higher extinction risk for the more northern population.</p></div></div></div><ul id=\"issue-navigation\" class=\"issue-navigation u-margin-s-bottom u-bg-grey1\"></ul>","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecochg.2023.100081","usgsCitation":"Kissel, A.M., Palen, W.J., Adams, M.J., and Garwood, J.M., 2024, Contrasting demographic responses under future climate for two populations of a montane amphibian: Climate Change Ecology, v. 7, 100081, 10 p., https://doi.org/10.1016/j.ecochg.2023.100081.","productDescription":"100081, 10 p.","ipdsId":"IP-115669","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":440801,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecochg.2023.100081","text":"Publisher Index Page"},{"id":430755,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California, Oregon, Washington","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -128.01194792245104,\n              50.743714435865\n            ],\n            [\n              -128.01194792245104,\n              36.39514683322275\n            ],\n            [\n              -115.61936979745127,\n              36.39514683322275\n            ],\n            [\n              -115.61936979745127,\n              50.743714435865\n            ],\n            [\n              -128.01194792245104,\n              50.743714435865\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"7","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kissel, Amanda M.","contributorId":211917,"corporation":false,"usgs":false,"family":"Kissel","given":"Amanda","email":"","middleInitial":"M.","affiliations":[{"id":36678,"text":"Simon Fraser University","active":true,"usgs":false}],"preferred":false,"id":905576,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Palen, Wendy J.","contributorId":211918,"corporation":false,"usgs":false,"family":"Palen","given":"Wendy","email":"","middleInitial":"J.","affiliations":[{"id":36678,"text":"Simon Fraser University","active":true,"usgs":false}],"preferred":false,"id":905577,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Adams, Michael J. 0000-0001-8844-042X","orcid":"https://orcid.org/0000-0001-8844-042X","contributorId":211916,"corporation":false,"usgs":true,"family":"Adams","given":"Michael","email":"","middleInitial":"J.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":905578,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Garwood, Justin M","contributorId":217674,"corporation":false,"usgs":false,"family":"Garwood","given":"Justin","email":"","middleInitial":"M","affiliations":[{"id":39681,"text":"California Dept fish wildlife","active":true,"usgs":false}],"preferred":false,"id":905579,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70252027,"text":"70252027 - 2024 - Major fluvial erosion and a 500-Mt sediment pulse triggered by lava-dam failure, Río Coca, Ecuador","interactions":[],"lastModifiedDate":"2024-03-11T12:02:54.212891","indexId":"70252027","displayToPublicDate":"2024-01-04T06:54:35","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"Major fluvial erosion and a 500-Mt sediment pulse triggered by lava-dam failure, Río Coca, Ecuador","docAbstract":"<div class=\"abstract-group  metis-abstract\"><div class=\"article-section__content en main\"><p>The failure of a 144-m-high lava-dam waterfall on the Río Coca, Ecuador, in February 2020 initiated a catastrophic watershed reset—regressive erosion upstream and a massive sediment pulse downstream—as the river evolves towards a new equilibrium grade. The evolution of this river corridor after a sudden base-level fall embodies the “complex response” concepts long understood through laboratory experiments, numerical modelling and smaller-scale field studies, but that have not been observed in the field before on this scale. This paper presents geomorphic and geotechnical data to characterize the evolution of the Río Coca since 2020. In the three years after the lava-dam failure, the erosion front migrated almost 13 km upstream along the mainstem river and triggered secondary headcuts that began migrating up tributaries. Erosion of the mainstem and tributary valleys generated a sediment pulse estimated to be 277 million m<sup>3</sup><span>&nbsp;</span>and ~500 million tonnes (Mt) over three years, depositing sediment tens of meters thick over tens of kilometres downstream from the former waterfall. This sediment pulse is one of the largest in modern times, comparable to the annual sediment load of a major continent-draining river but with orders-of-magnitude greater sediment yield. Geomorphic adjustment of the Río Coca represents a highly unusual natural disaster threatening life, property, water quality, the regional economy, major infrastructure and energy security. However, this event also provides a rare opportunity to learn how a large autogenic watershed disturbance and recovery evolve, with important lessons for interpreting the sedimentary record of volcanic landscapes.</p></div></div>","language":"English","publisher":"Wiley","doi":"10.1002/esp.5751","usgsCitation":"Barrera Crespo, P.D., Espinoza Giron, P., Bedoya, R., Gibson, S., East, A.E., Langendoen, E., and Boyd, P.M., 2024, Major fluvial erosion and a 500-Mt sediment pulse triggered by lava-dam failure, Río Coca, Ecuador: Earth Surface Processes and Landforms, v. 49, no. 3, p. 1058-1080, https://doi.org/10.1002/esp.5751.","productDescription":"23 p.","startPage":"1058","endPage":"1080","ipdsId":"IP-155338","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":440803,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/esp.5751","text":"Publisher Index Page"},{"id":426486,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Ecuador","otherGeospatial":"Río Coca","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -78.02554831641997,\n              -0.2842980721613628\n            ],\n            [\n              -78.02554831641997,\n              -1.4706593291238619\n            ],\n            [\n              -76.79508146704426,\n              -1.4706593291238619\n            ],\n            [\n              -76.79508146704426,\n              -0.2842980721613628\n            ],\n            [\n              -78.02554831641997,\n              -0.2842980721613628\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"49","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-01-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Barrera Crespo, Pedro D.","contributorId":334693,"corporation":false,"usgs":false,"family":"Barrera Crespo","given":"Pedro","email":"","middleInitial":"D.","affiliations":[{"id":80211,"text":"Corporacion Electrica del Ecuador","active":true,"usgs":false}],"preferred":false,"id":896276,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Espinoza Giron, Pablo","contributorId":334694,"corporation":false,"usgs":false,"family":"Espinoza Giron","given":"Pablo","email":"","affiliations":[{"id":80211,"text":"Corporacion Electrica del Ecuador","active":true,"usgs":false}],"preferred":false,"id":896277,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bedoya, Renan","contributorId":334695,"corporation":false,"usgs":false,"family":"Bedoya","given":"Renan","email":"","affiliations":[{"id":80211,"text":"Corporacion Electrica del Ecuador","active":true,"usgs":false}],"preferred":false,"id":896278,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gibson, Stanford","contributorId":334541,"corporation":false,"usgs":false,"family":"Gibson","given":"Stanford","email":"","affiliations":[{"id":13502,"text":"US Army Corps of Engineers","active":true,"usgs":false}],"preferred":false,"id":896279,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"East, Amy E. 0000-0002-9567-9460 aeast@usgs.gov","orcid":"https://orcid.org/0000-0002-9567-9460","contributorId":196364,"corporation":false,"usgs":true,"family":"East","given":"Amy","email":"aeast@usgs.gov","middleInitial":"E.","affiliations":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":896280,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Langendoen, Eddy J.","contributorId":256774,"corporation":false,"usgs":false,"family":"Langendoen","given":"Eddy J.","affiliations":[{"id":51861,"text":"USDA National Sedimentation Laboratory, Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":896281,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Boyd, Paul M","contributorId":215066,"corporation":false,"usgs":false,"family":"Boyd","given":"Paul","email":"","middleInitial":"M","affiliations":[{"id":590,"text":"U.S. Army Corps of Engineers","active":false,"usgs":false}],"preferred":false,"id":896282,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70250624,"text":"ofr20231097 - 2024 - Non-negligible near-term risk of extinction to the eastern migratory population of monarch butterflies—An updated assessment (2006–22)","interactions":[],"lastModifiedDate":"2024-01-25T20:08:49.8811","indexId":"ofr20231097","displayToPublicDate":"2024-01-03T10:09:17","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2023-1097","displayTitle":"Non-Negligible Near-Term Risk of Extinction to the Eastern Migratory Population of Monarch Butterflies—An Updated Assessment (2006–22)","title":"Non-negligible near-term risk of extinction to the eastern migratory population of monarch butterflies—An updated assessment (2006–22)","docAbstract":"<p>The eastern migratory population of monarch butterflies (<i>Danaus plexippus</i>) started declining as early as the mid-1970s and seemed to stop declining by the early 2000s; the population now (about 2022) persists at a much-reduced abundance. Stochastic variation in abundance, at levels typical of monarch butterflies and other insects, was assessed to determine whether this population is at heightened risk of quasi-extinction, a level of abundance below which recovery of the migratory behavior is uncertain. Using previously published Bayesian state-space modeling methods it was determined roughly equivalent risk of quasi-extinction as was reported in 2016 for the species (28.7 percent [1.9–81.0 credible interval] and 52.0 percent [3.2–97.7 credible interval] at the 10- and 20-year marks, respectively). Though highly uncertain, the risk is non-negligibly positive. Warning signal analysis indicates the current dynamic is dominated by stochastic variation, which seems to be heightening risk with the passage of time. Increasing breeding opportunities through restoration of milkweed in its northern breeding locations seems to be the most promising means of mitigating extinction risk for this species.</p>","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231097","usgsCitation":"Thogmartin, W.E., 2024, Non-negligible near-term risk of extinction to the eastern migratory population of monarch butterflies—An updated assessment (2006–22): U.S. Geological Survey Open-File Report 2023–1097, 10 p., https://doi.org/10.3133/ofr20231097.","productDescription":"Report: iii, 10 p.; Data Release","numberOfPages":"18","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-152775","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":423797,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9WRARO7","text":"USGS data release","linkHelpText":"Eastern migratory monarch butterfly population estimates and associated early warning signals (2006–22)"},{"id":423796,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1097/images/"},{"id":423798,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231097/full"},{"id":423795,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1097/ofr20231097.XML"},{"id":423794,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1097/ofr20231097.pdf","text":"Report","size":"949 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023–1097"},{"id":423793,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1097/coverthb.jpg"}],"contact":"<p>Director, <a href=\"https://www.usgs.gov/centers/upper-midwest-environmental-sciences-center\" data-mce-href=\"https://www.usgs.gov/centers/upper-midwest-environmental-sciences-center\">Upper Midwest Environmental Sciences Center</a><br>U.S. Geological Survey<br>2630 Fanta Reed Road<br>La Crosse, Wisconsin 54603</p><p><a href=\"https://pubs.usgs.gov/contact\" data-mce-href=\"../contact\">Contact Pubs Warehouse</a></p>","tableOfContents":"<ul><li>Abstract</li><li>Introduction</li><li>Methods</li><li>Results</li><li>Discussion</li><li>Acknowledgments</li><li>References Cited</li><li>Appendix 1</li></ul>","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"publishedDate":"2024-01-03","noUsgsAuthors":false,"publicationDate":"2024-01-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Thogmartin, Wayne E. 0000-0002-2384-4279 wthogmartin@usgs.gov","orcid":"https://orcid.org/0000-0002-2384-4279","contributorId":2545,"corporation":false,"usgs":true,"family":"Thogmartin","given":"Wayne","email":"wthogmartin@usgs.gov","middleInitial":"E.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":890608,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}}
,{"id":70250774,"text":"70250774 - 2024 - Slope Unit Maker (SUMak): An efficient and parameter-free algorithm for delineating slope units to improve landslide modeling","interactions":[],"lastModifiedDate":"2024-01-04T12:58:28.072553","indexId":"70250774","displayToPublicDate":"2024-01-03T06:55:05","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":17114,"text":"Natural Hazards and Earth Systems Sciences (NHESS)","active":true,"publicationSubtype":{"id":10}},"title":"Slope Unit Maker (SUMak): An efficient and parameter-free algorithm for delineating slope units to improve landslide modeling","docAbstract":"<div id=\"abstract\" class=\"abstract sec\"><div class=\"abstract-content show-no-js\"><p id=\"d1e148\">Slope units are terrain partitions bounded by drainage and divide lines. In landslide modeling, including susceptibility modeling and event-specific modeling of landslide occurrence, slope units provide several advantages over gridded units, such as better capturing terrain geometry, improved incorporation of geospatial landslide-occurrence data in different formats (e.g., point and polygon), and better accommodating the varying data accuracy and precision in landslide inventories. However, the use of slope units in regional (<span class=\"inline-formula\"><i>&gt;</i></span> 100 km<span class=\"inline-formula\"><sup>2</sup></span>) landslide studies remains limited due, in part, to the large computational costs and/or poor reproducibility with current delineation methods. We introduce a computationally efficient algorithm for the parameter-free delineation of slope units that leverages tools from within TauDEM and GRASS, using an R interface. The algorithm uses geomorphic laws to define the appropriate scaling of the slope units representative of hillslope processes, avoiding the often ambiguous determination of slope unit size. We then demonstrate how slope units enable more robust regional-scale landslide susceptibility and event-specific landslide occurrence maps.</p></div></div><div id=\"citation-footer\" class=\"sec\"><br></div>","language":"English","publisher":"European Geophysical Union","doi":"10.5194/nhess-24-1-2024","collaboration":"Oregon State, Kentucky Geological Survey","usgsCitation":"Woodard, J.B., Mirus, B., Wood, N.J., Allstadt, K.E., Leshchinsky, B., and Crawford, M., 2024, Slope Unit Maker (SUMak): An efficient and parameter-free algorithm for delineating slope units to improve landslide modeling: Natural Hazards and Earth Systems Sciences (NHESS), v. 24, no. 1, p. 1-12, https://doi.org/10.5194/nhess-24-1-2024.","productDescription":"12 p.","startPage":"1","endPage":"12","ipdsId":"IP-146317","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":440808,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/nhess-24-1-2024","text":"Publisher Index Page"},{"id":424109,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"24","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-01-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Woodard, Jacob Bryson 0000-0002-3095-0774","orcid":"https://orcid.org/0000-0002-3095-0774","contributorId":305507,"corporation":false,"usgs":true,"family":"Woodard","given":"Jacob","email":"","middleInitial":"Bryson","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":891372,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mirus, Benjamin B. 0000-0001-5550-014X","orcid":"https://orcid.org/0000-0001-5550-014X","contributorId":267912,"corporation":false,"usgs":true,"family":"Mirus","given":"Benjamin B.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":891373,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wood, Nathan J. 0000-0002-6060-9729 nwood@usgs.gov","orcid":"https://orcid.org/0000-0002-6060-9729","contributorId":3347,"corporation":false,"usgs":true,"family":"Wood","given":"Nathan","email":"nwood@usgs.gov","middleInitial":"J.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":891374,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Allstadt, Kate E. 0000-0003-4977-5248","orcid":"https://orcid.org/0000-0003-4977-5248","contributorId":138704,"corporation":false,"usgs":true,"family":"Allstadt","given":"Kate","email":"","middleInitial":"E.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":891375,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Leshchinsky, Ben","contributorId":332926,"corporation":false,"usgs":false,"family":"Leshchinsky","given":"Ben","affiliations":[{"id":6680,"text":"Oregon State University","active":true,"usgs":false}],"preferred":false,"id":891376,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Crawford, Matthew","contributorId":332927,"corporation":false,"usgs":false,"family":"Crawford","given":"Matthew","affiliations":[{"id":40489,"text":"Kentucky Geological Survey","active":true,"usgs":false}],"preferred":false,"id":891377,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70251172,"text":"70251172 - 2024 - Global potential distribution of mangroves: Taking into account salt marsh interactions along latitudinal gradients","interactions":[],"lastModifiedDate":"2024-01-25T12:44:18.618329","indexId":"70251172","displayToPublicDate":"2024-01-03T06:39:41","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Global potential distribution of mangroves: Taking into account salt marsh interactions along latitudinal gradients","docAbstract":"<p>Mangrove is one of the most productive and sensitive ecosystems in the world. Due to the complexity and specificity of mangrove habitat, the development of mangrove is regulated by several factors. Species distribution models (SDMs) are effective tools to identify the potential habitats for establishing and regenerating the ecosystem. Such models usually include exclusively environmental factors. Nevertheless, recent studies have challenged this notion and highlight the importance of including biotic interactions. Both factors are necessary for a mechanistic understanding of the mangrove distribution in order to promote the protection and restoration of mangroves. Thus, we present a novel approach of combining environmental factors and interactions with salt marsh for projecting mangrove distributions at the global level and within latitudinal zones. To test the salt marsh interaction, we fit the MaxEnt model with two predicting sets: (1) environments only and (2) environments + salt marsh interaction index (SII). We found that both sets of models had good predictive ability, although the SII improved model performance slightly. Potential distribution areas of mangrove decrease with latitudes, and are controlled by biotic and abiotic factors. Temperature, precipitation and wind speed are generally critical at both global scale and ecotones along latitudes. SII is important on global scale, with a contribution of 5.9%, ranking 6th, and is particularly critical in the 10–30°S and 20–30°N zone. Interactions with salt marsh, including facilitation and competition, are shown to affect the distribution of mangroves at the zone of coastal ecotone, especially in the latitudinal range from 10° - 30°. The contribution of SII to mangrove distribution increases with latitudes due to the difference in the adaptive capacity of salt marsh plants and mangroves to environments. Totally, this study identified and quantified the effects of salt marsh on mangrove distribution by establishing the SII. The results not only facilitate to establish a more accurate mangrove distribution map, but also improve the efficiency of mangrove restoration by considering the salt marsh interaction in the mangrove management projects. In addition, the method of incorporating biotic interaction into SDMs through establish the biotic interaction index has contributed to the development of SDMs.</p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2023.119892","usgsCitation":"Cui, L., DeAngelis, D., Berger, U., Cao, M., Zhang, Y., Zhang, X., and Jiang, J., 2024, Global potential distribution of mangroves: Taking into account salt marsh interactions along latitudinal gradients: Journal of Environmental Management, v. 351, 119892, 13 p., https://doi.org/10.1016/j.jenvman.2023.119892.","productDescription":"119892, 13 p.","ipdsId":"IP-142484","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":424944,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"351","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Cui, Lina","contributorId":333612,"corporation":false,"usgs":false,"family":"Cui","given":"Lina","email":"","affiliations":[{"id":79946,"text":"Nanjing Forestry University","active":true,"usgs":false}],"preferred":false,"id":893338,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"DeAngelis, Don 0000-0002-1570-4057","orcid":"https://orcid.org/0000-0002-1570-4057","contributorId":221357,"corporation":false,"usgs":true,"family":"DeAngelis","given":"Don","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":893339,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Berger, Uta","contributorId":224016,"corporation":false,"usgs":false,"family":"Berger","given":"Uta","affiliations":[{"id":40811,"text":"TU Dresden, Institute of Forest Growth and Computer Science, Germany","active":true,"usgs":false}],"preferred":false,"id":893340,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cao, Minmin","contributorId":333613,"corporation":false,"usgs":false,"family":"Cao","given":"Minmin","email":"","affiliations":[{"id":79946,"text":"Nanjing Forestry University","active":true,"usgs":false}],"preferred":false,"id":893341,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Zhang, Yaqi","contributorId":333614,"corporation":false,"usgs":false,"family":"Zhang","given":"Yaqi","email":"","affiliations":[{"id":79946,"text":"Nanjing Forestry University","active":true,"usgs":false}],"preferred":false,"id":893342,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Zhang, Xiaomin","contributorId":333615,"corporation":false,"usgs":false,"family":"Zhang","given":"Xiaomin","email":"","affiliations":[{"id":79948,"text":"Zhejiang Academy of Forestry","active":true,"usgs":false}],"preferred":false,"id":893343,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Jiang, Jiang","contributorId":191968,"corporation":false,"usgs":false,"family":"Jiang","given":"Jiang","email":"","affiliations":[],"preferred":false,"id":893344,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70250880,"text":"70250880 - 2024 - Estimating lithium concentrations in groundwater used as drinking water for the conterminous United States","interactions":[],"lastModifiedDate":"2024-01-25T14:57:06.787905","indexId":"70250880","displayToPublicDate":"2024-01-02T10:47:01","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5925,"text":"Environmental Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Estimating lithium concentrations in groundwater used as drinking water for the conterminous United States","docAbstract":"<p><span>Lithium (Li) concentrations in drinking-water supplies are not regulated in the United States; however, Li is included in the 2022 U.S. Environmental Protection Agency list of unregulated contaminants for monitoring by public water systems. Li is used pharmaceutically to treat bipolar disorder, and studies have linked its occurrence in drinking water to human-health outcomes. An extreme gradient boosting model was developed to estimate geogenic Li in drinking-water supply wells throughout the conterminous United States. The model was trained using Li measurements from ∼13,500 wells and predictor variables related to its natural occurrence in groundwater. The model predicts the probability of Li in four concentration classifications, ≤4 μg/L, &gt;4 to ≤10 μg/L, &gt;10 to ≤30 μg/L, and &gt;30 μg/L. Model predictions were evaluated using wells held out from model training and with new data and have an accuracy of 47–65%. Important predictor variables include average annual precipitation, well depth, and soil geochemistry. Model predictions were mapped at a spatial resolution of 1 km</span><sup>2</sup><span>&nbsp;and represent well depths associated with public- and private-supply wells. This model was developed by hydrologists and public-health researchers to estimate Li exposure from drinking water and compare to national-scale human-health data for a better understanding of dose–response to low (&lt;30 μg/L) concentrations of Li.</span></p>","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.3c03315","usgsCitation":"Lombard, M.A., Brown, E.E., Saftner, D., Arienzo, M.M., Fuller-Thomson, E., Brown, C., and Ayotte, J.D., 2024, Estimating lithium concentrations in groundwater used as drinking water for the conterminous United States: Environmental Science and Technology, v. 58, no. 2, p. 1255-1264, https://doi.org/10.1021/acs.est.3c03315.","productDescription":"10 p.","startPage":"1255","endPage":"1264","ipdsId":"IP-152446","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":440811,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index 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            -124.53284,\n                42.76599\n              ],\n              [\n                -124.14214,\n                43.70838\n              ],\n              [\n                -124.02053,\n                44.6159\n              ],\n              [\n                -123.89893,\n                45.52341\n              ],\n              [\n                -124.07963,\n                46.86475\n              ],\n              [\n                -124.39567,\n                47.72017\n              ],\n              [\n                -124.68721,\n                48.18443\n              ],\n              [\n                -124.5661,\n                48.37971\n              ],\n              [\n                -123.12,\n                48.04\n              ],\n              [\n                -122.58736,\n                47.096\n              ],\n              [\n                -122.34,\n                47.36\n              ],\n              [\n                -122.5,\n                48.18\n              ],\n              [\n                -122.84,\n                49\n              ],\n              [\n                -120,\n                49\n              ],\n              [\n                -117.03121,\n                49\n              ],\n              [\n                -116.04818,\n                49\n              ],\n              [\n                -113,\n                49\n              ],\n              [\n                -110.05,\n                49\n              ],\n              [\n                -107.05,\n                49\n              ],\n              [\n                -104.04826,\n                48.99986\n              ],\n              [\n                -100.65,\n                49\n              ],\n              [\n                -97.22872,\n                49.0007\n              ],\n              [\n                -95.15907,\n                49\n              ],\n              [\n                -95.15609,\n                49.38425\n              ],\n              [\n                -94.81758,\n                49.38905\n              ]\n            ]\n          ]\n        ]\n      },\n      \"properties\": {\n        \"name\": \"United States\"\n      }\n    }\n  ]\n}","volume":"58","issue":"2","noUsgsAuthors":false,"publicationDate":"2024-01-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Lombard, Melissa A. 0000-0001-5924-6556 mlombard@usgs.gov","orcid":"https://orcid.org/0000-0001-5924-6556","contributorId":198254,"corporation":false,"usgs":true,"family":"Lombard","given":"Melissa","email":"mlombard@usgs.gov","middleInitial":"A.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"preferred":true,"id":891896,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brown, Eric E.","contributorId":333096,"corporation":false,"usgs":false,"family":"Brown","given":"Eric","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":891897,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Saftner, Daniel","contributorId":333090,"corporation":false,"usgs":false,"family":"Saftner","given":"Daniel","email":"","affiliations":[{"id":16138,"text":"Desert Research Institute","active":true,"usgs":false}],"preferred":false,"id":891898,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Arienzo, Monica M.","contributorId":333091,"corporation":false,"usgs":false,"family":"Arienzo","given":"Monica","email":"","middleInitial":"M.","affiliations":[{"id":16138,"text":"Desert Research Institute","active":true,"usgs":false}],"preferred":false,"id":891899,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fuller-Thomson, Esme","contributorId":333092,"corporation":false,"usgs":false,"family":"Fuller-Thomson","given":"Esme","email":"","affiliations":[{"id":7044,"text":"University of Toronto","active":true,"usgs":false}],"preferred":false,"id":891900,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brown, Craig J. 0000-0002-3858-3964","orcid":"https://orcid.org/0000-0002-3858-3964","contributorId":210450,"corporation":false,"usgs":true,"family":"Brown","given":"Craig J.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":891901,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Ayotte, Joseph D. 0000-0002-1892-2738 jayotte@usgs.gov","orcid":"https://orcid.org/0000-0002-1892-2738","contributorId":149619,"corporation":false,"usgs":true,"family":"Ayotte","given":"Joseph","email":"jayotte@usgs.gov","middleInitial":"D.","affiliations":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":405,"text":"NH/VT office of New England Water Science Center","active":true,"usgs":true}],"preferred":true,"id":891902,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}}
,{"id":70256598,"text":"70256598 - 2024 - Rapid estimation of single-station earthquake magnitudes with machine learning on a global scale","interactions":[],"lastModifiedDate":"2024-08-01T14:48:54.943118","indexId":"70256598","displayToPublicDate":"2024-01-02T09:45:19","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1135,"text":"Bulletin of the Seismological Society of America","onlineIssn":"1943-3573","printIssn":"0037-1106","active":true,"publicationSubtype":{"id":10}},"title":"Rapid estimation of single-station earthquake magnitudes with machine learning on a global scale","docAbstract":"<p><span>The foundation of earthquake monitoring is the ability to rapidly detect, locate, and estimate the size of seismic sources. Earthquake magnitudes are particularly difficult to rapidly characterize because magnitude types are only applicable to specific magnitude ranges, and location errors propagate to substantial magnitude errors. We developed a method for rapid estimation of single‐station earthquake magnitudes using raw three‐component&nbsp;</span><i>P</i><span>&nbsp;waveforms observed at local to teleseismic distances, independent of prior size or location information. We used the MagNet regression model architecture (</span><a class=\"link link-ref xref-bibr\" data-modal-source-id=\"rf28\">Mousavi and Beroza, 2020b</a><span>), which combines convolutional and recurrent neural networks. We trained our model using ∼2.4 million&nbsp;</span><i>P</i><span>‐phase arrivals labeled by the authoritative magnitude assigned by the U.S. Geological Survey. We tested input data parameters (e.g., window length) that could affect the performance of our model in near‐real‐time monitoring applications. At the longest waveform window length of 114&nbsp;s, our model (Artificial Intelligence Magnitude [AIMag]) is accurate (median estimated magnitude within ±0.5 magnitude units from catalog magnitude) between&nbsp;</span><strong>M</strong><span>&nbsp;2.3 and 7.6. However, magnitudes above&nbsp;</span><strong>M</strong><span>&nbsp;∼7 are more underestimated as true magnitude increases. As the windows are shortened down to 1&nbsp;s, the point at which higher magnitudes begin to be underestimated moves toward lower magnitudes, and the degree of underestimation increases. The over and underestimation of magnitudes for the smallest and largest earthquakes, respectively, are potentially related to the limited number of events in these ranges within the training data, as well as magnitude saturation effects related to not capturing the full source time function of large earthquakes. Importantly, AIMag can determine earthquake magnitudes with individual stations’ waveforms without instrument response correction or knowledge of an earthquake’s source‐station distance. This work may enable monitoring agencies to more rapidly recognize large, potentially tsunamigenic global earthquakes from few stations, allowing for faster event processing and reporting. This is critical for timely warnings for seismic‐related hazards.</span></p>","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0120230171","usgsCitation":"Dybing, S., Yeck, W.L., Cole, H.M., and Melgar, D., 2024, Rapid estimation of single-station earthquake magnitudes with machine learning on a global scale: Bulletin of the Seismological Society of America, v. 114, no. 3, p. 1523-1538, https://doi.org/10.1785/0120230171.","productDescription":"16 p.","startPage":"1523","endPage":"1538","ipdsId":"IP-158857","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":432030,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"114","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-01-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Dybing, Sydney","contributorId":341314,"corporation":false,"usgs":false,"family":"Dybing","given":"Sydney","email":"","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":908222,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yeck, William L. 0000-0002-2801-8873 wyeck@usgs.gov","orcid":"https://orcid.org/0000-0002-2801-8873","contributorId":147558,"corporation":false,"usgs":true,"family":"Yeck","given":"William","email":"wyeck@usgs.gov","middleInitial":"L.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true},{"id":309,"text":"Geology and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":908223,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Cole, Hank M. 0000-0003-1684-9116","orcid":"https://orcid.org/0000-0003-1684-9116","contributorId":335228,"corporation":false,"usgs":true,"family":"Cole","given":"Hank","email":"","middleInitial":"M.","affiliations":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"preferred":true,"id":908224,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Melgar, Diego","contributorId":341315,"corporation":false,"usgs":false,"family":"Melgar","given":"Diego","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":908225,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
,{"id":70256115,"text":"70256115 - 2024 - Mass-balance-consistent geological stock accounting: A new approach toward sustainable management of mineral resources","interactions":[],"lastModifiedDate":"2024-07-23T13:40:07.15414","indexId":"70256115","displayToPublicDate":"2024-01-02T08:33:44","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5925,"text":"Environmental Science and Technology","active":true,"publicationSubtype":{"id":10}},"title":"Mass-balance-consistent geological stock accounting: A new approach toward sustainable management of mineral resources","docAbstract":"<p><span>Global resource extraction raises concerns about environmental pressures and the security of mineral supply. Strategies to address these concerns depend on robust information on natural resource endowments, and on suitable methods to monitor and model their changes over time. However, current mineral resources and reserves reporting and accounting workflows are poorly suited for addressing mineral depletion or answering questions about the long-term sustainable supply. Our integrative review finds that the lack of a robust theoretical concept and framework for mass-balance (MB)-consistent geological stock accounting hinders systematic industry-government data integration, resource governance, and strategy development. We evaluate the existing literature on geological stock accounting, identify shortcomings of current monitoring of mine production, and outline a conceptual framework for MB-consistent system integration based on material flow analysis (MFA). Our synthesis shows that recent developments in Earth observation, geoinformation management, and sustainability reporting act as catalysts that make MB-consistent geological stock accounting increasingly feasible. We propose first steps for its implementation and anticipate that our perspective as “resource realists” will facilitate the integration of geological and anthropogenic material systems, help secure future mineral supply, and support the global sustainability transition.</span></p>","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.3c03088","usgsCitation":"Simoni, M.U., Drielsma, J.A., Ericsson, M., Gunn, A.G., Heiberg, S., Heldal, T.A., Nassar, N.T., Petavratz, E., and Muller, D.B., 2024, Mass-balance-consistent geological stock accounting: A new approach toward sustainable management of mineral resources: Environmental Science and Technology, v. 58, p. 971-990, https://doi.org/10.1021/acs.est.3c03088.","productDescription":"20 p.","startPage":"971","endPage":"990","ipdsId":"IP-145992","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":440814,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.3c03088","text":"Publisher Index Page"},{"id":431350,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"58","noUsgsAuthors":false,"publicationDate":"2024-01-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Simoni, Mark U.","contributorId":340251,"corporation":false,"usgs":false,"family":"Simoni","given":"Mark","email":"","middleInitial":"U.","affiliations":[{"id":81520,"text":"Norwegian University of Science and Technology, Norway","active":true,"usgs":false}],"preferred":false,"id":906749,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Drielsma, Johannes A.","contributorId":340252,"corporation":false,"usgs":false,"family":"Drielsma","given":"Johannes","email":"","middleInitial":"A.","affiliations":[{"id":81521,"text":"Drielsma Resources Europe, Germany","active":true,"usgs":false}],"preferred":false,"id":906750,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ericsson, Magnus","contributorId":340253,"corporation":false,"usgs":false,"family":"Ericsson","given":"Magnus","email":"","affiliations":[{"id":81522,"text":"Luleå University of Technology, Sweden","active":true,"usgs":false}],"preferred":false,"id":906751,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Gunn, Andrew G.","contributorId":340254,"corporation":false,"usgs":false,"family":"Gunn","given":"Andrew","email":"","middleInitial":"G.","affiliations":[{"id":25567,"text":"British Geological Survey","active":true,"usgs":false}],"preferred":false,"id":906752,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Heiberg, Sigurd","contributorId":340255,"corporation":false,"usgs":false,"family":"Heiberg","given":"Sigurd","email":"","affiliations":[{"id":81523,"text":"Petronavit AS, Norway","active":true,"usgs":false}],"preferred":false,"id":906753,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Heldal, Tom A.","contributorId":340256,"corporation":false,"usgs":false,"family":"Heldal","given":"Tom","email":"","middleInitial":"A.","affiliations":[{"id":35509,"text":"Geological Survey of Norway","active":true,"usgs":false}],"preferred":false,"id":906754,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Nassar, Nedal T. 0000-0001-8758-9732 nnassar@usgs.gov","orcid":"https://orcid.org/0000-0001-8758-9732","contributorId":197864,"corporation":false,"usgs":true,"family":"Nassar","given":"Nedal","email":"nnassar@usgs.gov","middleInitial":"T.","affiliations":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"preferred":true,"id":906755,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Petavratz, Evi","contributorId":340257,"corporation":false,"usgs":false,"family":"Petavratz","given":"Evi","email":"","affiliations":[{"id":25567,"text":"British Geological Survey","active":true,"usgs":false}],"preferred":false,"id":906756,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Muller, Daniel B.","contributorId":340258,"corporation":false,"usgs":false,"family":"Muller","given":"Daniel","email":"","middleInitial":"B.","affiliations":[{"id":39348,"text":"Norwegian University of Science and Technology","active":true,"usgs":false}],"preferred":false,"id":906757,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}}
,{"id":70251342,"text":"70251342 - 2024 - Identifying conditions where reed canarygrass (Phalaris arundinacea) functions as a driver of forest loss in the Upper Mississippi River floodplain under different hydrological scenarios","interactions":[],"lastModifiedDate":"2024-02-06T13:19:35.568516","indexId":"70251342","displayToPublicDate":"2024-01-02T07:15:42","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3751,"text":"Wetlands Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Identifying conditions where reed canarygrass (Phalaris arundinacea) functions as a driver of forest loss in the Upper Mississippi River floodplain under different hydrological scenarios","docAbstract":"<div id=\"Abs1-section\" class=\"c-article-section\"><div id=\"Abs1-content\" class=\"c-article-section__content\"><p>Most of the world’s river-floodplain ecosystems are simultaneously undergoing modifications to their hydrological regimes and experiencing species invasions, making it unclear whether invasive species are the main drivers of ecosystem change or simply responding to changes in the hydrological regime.</p><p>We simulated patterns of forest recruitment and succession in a 2500-ha portion of the Upper Mississippi River floodplain with and without removal of invasive<span>&nbsp;</span><i>Phalaris arundinacea</i><span>&nbsp;</span>and under two different future 100-year hydrological scenarios: a future maintaining the average flooding conditions of the past 40 years (random) and a future that projects an observed upward 40-year trend in flooding conditions forward (trending). By comparing scenarios that included<span>&nbsp;</span><i>Phalaris</i><span>&nbsp;</span>removal and ones that did not, we were able to identify the conditions where<span>&nbsp;</span><i>Phalaris</i><span>&nbsp;</span>was the main driver of forest loss vs. the conditions where hydrology was the main driver of forest loss. Areas where<span>&nbsp;</span><i>Phalaris</i><span>&nbsp;</span>was the main driver of forest loss had mean annual flood inundation durations that were similar to areas that did not lose forest cover (60–90 growing season days), while areas where flooding was the main driver of forest loss had longer mean inundation durations (102–124 growing season days). In comparison to the random hydrology scenario, the trending scenario produced a decrease in the area over which<span>&nbsp;</span><i>Phalaris</i><span>&nbsp;</span>was identified as the main driver of forest loss and an increase in the area over which flood inundation was identified as the main driver of forest loss. Thus, if the observed trends in flooding continue, our model projects an increase in the area over which eradicating<span>&nbsp;</span><i>Phalaris</i><span>&nbsp;</span>is unlikely to result in the maintenance of forest cover. We used the Resist-Accept-Direct (RAD) framework to discuss potential management options to resist changes and maintain forest cover where<span>&nbsp;</span><i>Phalaris</i><span>&nbsp;</span>is likely to be the main driver of forest loss and to accept or direct changes in areas where forest loss is likely driven by hydrological change.</p></div></div>","language":"English","publisher":"Springer Nature","doi":"10.1007/s11273-023-09969-6","usgsCitation":"De Jager, N.R., Rohweder, J.J., Van Appledorn, M., Hlavacek, E., and Meier, A., 2024, Identifying conditions where reed canarygrass (Phalaris arundinacea) functions as a driver of forest loss in the Upper Mississippi River floodplain under different hydrological scenarios: Wetlands Ecology and Management, v. 32, p. 153-170, https://doi.org/10.1007/s11273-023-09969-6.","productDescription":"18 p.","startPage":"153","endPage":"170","ipdsId":"IP-149601","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":435067,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P971TC5G","text":"USGS data release","linkHelpText":"Identifying conditions where reed canarygrass (Phalaris arundinacea) functions as a driver of forest loss in the Upper Mississippi River floodplain under different hydrological scenarios"},{"id":425437,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Iowa, Minnesota, Wisconsin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -91.3876472088107,\n              43.699879451781044\n            ],\n            [\n              -91.3876472088107,\n              43.281352841078245\n            ],\n            [\n              -91.02775519900284,\n              43.281352841078245\n            ],\n            [\n              -91.02775519900284,\n              43.699879451781044\n            ],\n            [\n              -91.3876472088107,\n              43.699879451781044\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"32","noUsgsAuthors":false,"publicationDate":"2024-01-02","publicationStatus":"PW","contributors":{"authors":[{"text":"De Jager, Nathan R. 0000-0002-6649-4125 ndejager@usgs.gov","orcid":"https://orcid.org/0000-0002-6649-4125","contributorId":3717,"corporation":false,"usgs":true,"family":"De Jager","given":"Nathan","email":"ndejager@usgs.gov","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":894163,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rohweder, Jason J. 0000-0001-5131-9773 jrohweder@usgs.gov","orcid":"https://orcid.org/0000-0001-5131-9773","contributorId":150539,"corporation":false,"usgs":true,"family":"Rohweder","given":"Jason","email":"jrohweder@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":894164,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Van Appledorn, Molly 0000-0002-8029-0014","orcid":"https://orcid.org/0000-0002-8029-0014","contributorId":205785,"corporation":false,"usgs":true,"family":"Van Appledorn","given":"Molly","email":"","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":894165,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hlavacek, Enrika 0000-0002-9872-2305","orcid":"https://orcid.org/0000-0002-9872-2305","contributorId":297184,"corporation":false,"usgs":false,"family":"Hlavacek","given":"Enrika","affiliations":[{"id":48800,"text":"Former USGS, UMESC employee","active":true,"usgs":false}],"preferred":false,"id":894166,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Meier, Andy","contributorId":333863,"corporation":false,"usgs":false,"family":"Meier","given":"Andy","email":"","affiliations":[{"id":79993,"text":"U.S. Army Corps of Engineers (USACE)","active":true,"usgs":false}],"preferred":false,"id":894167,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70259501,"text":"70259501 - 2024 - Snake River Fall Chinook Salmon research and monitoring","interactions":[],"lastModifiedDate":"2024-10-10T16:16:16.36293","indexId":"70259501","displayToPublicDate":"2024-01-01T10:59:34","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Snake River Fall Chinook Salmon research and monitoring","docAbstract":"In Chapter 1, we report on development and application of an integrated population model (IPM) for the natural-origin fall Chinook salmon population upstream of Lower Granite Dam.  This year’s efforts represent the third update to the model.  Initial efforts focused on generating juvenile and adult abundance estimates, with estimates of uncertainty, for informing the life-cycle model and estimating the effects of covariates on key demographic parameters. The goals of this year’s report are to 1) describe the modifications and advances made since the previous report, 2) to annually update and report the abundance estimates and other quantities used in the model, 3) to provide annual estimates of population parameters estimated by the IPM, and 4) to outline the next year’s tasks for advancing and/or applying the model.\n Since our last report on the life-cycle model, we have made a number of changes including: 1) incorporating jack abundance and age-structure data into the observation model, 2) changing smolt-to-adult survival (SAR) for subyearling and yearling to partial SARs that represent the joint probability surviving and entering the ocean at a given juvenile age, 3) combining age categories for rarely observed ages, 4) using scale data from unmarked fish to estimate age structure, and 5) generating composite life-cycle demographic parameters (cumulative capacity and productivity) from stage-specific parameters.  We also generated juvenile abundance estimates, extended the model to include three additional brood years (1992– 2021), and ran the model to forecast returns to Lower Granite in 2022. \n For posterior medians of life stage-specific parameters, we estimated a mean productivity of 438 natural-origin juvenile recruits per female spawner, a capacity of 1.36 million juveniles, and a mean smolt-to-adult survival (SAR) of 1.2%.  We detected strong density-dependent regulation, with juvenile recruits per spawner declining to about 50 juvenile recruits per female spawner at high spawner abundance.  Across the entire life cycle, these stage-specific parameters resulted in a median cumulative intrinsic productivity of 1.93 adult female recruits per female spawner and a median equilibrium abundance of 2,851 female spawners (7,842 total spawners).  Annual juvenile productivity varied from about 250–1,000 juveniles per spawner but displayed no temporal trends or patterns.  For the three most recent brood years added to the model, recruits per spawner were higher than average but well within the range of uncertainty observed over the entire time series.  In contrast to juvenile recruitment variability, SAR varied considerably among years and exhibited two periods of high survival (1996–2001 and 2007–2012) when SAR ranged from 2% to 6% and cumulative productivity ranged from 2 to 8 recruits per spawner. Partial SARs revealed that yearling outmigrants contributed substantially to the high SARs in the first high-survival period, but the second period was dominated by subyearlings.  Yearlings contributed >30% to SAR in most years prior to 2007, and <30% since 2007.\n\nOur two-stage IPM provides a wealth of information about population dynamics affecting two key life-stage transitions (spawner to juvenile, and juvenile to spawner) centered on passage at Lower Granite Dam. By summarizing these stage-specific demographic parameters across the entire life cycle, this information will be useful for informing the recovery status of this threatened population.  Whereas previous versions introduced hydrosystem and ocean covariates into the model, this phase of model development focused on solidifying the underlying model structure by introducing the concept of partial SARs and developing composite productivity and capacity as a function of underlying stage-specific parameters.  Given this advancement, our next steps are to re-incorporate covariates into the model, specifically to understand how different factors affect partial SARs of subyearling and yearlings.  Longer term model developments include:1) incorporating hatchery fish to explicitly estimate their survival as an alternative method for estimating natural-origin age composition, 2) expanding the model’s structure to include the three major spawning aggregates, 3) more explicitly modeling hydrosystem effects including transportation, and 4) using the model to assess retrospective and prospective management actions.\n\nIn 2022, the U.S. Geological Survey (USGS) focused adult salmon survey efforts in the Snake River on deepwater redd searches and fish collection for parentage-based tagging (PBT) analyses. We use used a boat-mounted underwater video camera to count 99 deepwater redds at 16 of the 29 sites surveyed. Redd depths averaged 4.4 m. In conjunction with the Idaho Power Company, we collected genetic samples from 318 live fall Chinook salmon (Oncorhynchus tshawytscha) and 19 carcasses at 40 unique geographic locations that spanned 91 river kilometers. Eighty fish were collected at three sites (High Range [rkm 332.3], Dug Bar [rkm 315.4], and Three Creek [rkm 384.0]), which accounted for 23% of all collected fish in 2022. Most (333 fish) post-spawned salmon were collected from early to mid-November just after the peak of spawning. A summary of 2021 PBT results produced by the Idaho Power Company can be found in Appendix A.2.\n\nBeach seining and PIT tagging of subyearling fall Chinook salmon was conducted in Snake and Salmon rivers to obtain information on population metrics and growth as well as to provide data for ongoing life-cycle modeling. In the Snake River, we collected 7,496 subyearlings, tagged 4,139, and recaptured 502 (12.1%). Using 8-mm tags in 45–49-mm fish allowed us to represent an additional 25% of the juvenile population through PIT tagging beyond just using standard 9- and 12-mm tags. In the Salmon River, we captured 206 natural subyearlings with the majority (52%) of fish being captured at two sites: rkm 20 and 26. We tagged 145 subyearlings and recaptured 9 fish. \n\nMany of the subyearlings we tagged in the Snake River were detected passing Lower Granite Dam, but only 4 fish tagged in the Salmon River were detected. In total we detected 484 (11.3%) tagged fish at Lower Granite Dam, and detection rates varied by tag size and passage route. More subyearlings were detected passing via the removable spill weir (RSW) earlier in the season while more fish were detected passing through the juvenile fish bypass system (JBS) earlier in the season while more fish were detected passing via the removable spill weir (RSW) later in the season. In general, fish tagged with 12-mm PIT tags had higher detection rates than fish tagged with smaller tags. Survival to Lower Granite Dam was low and ranged from 0.22 to 0.36. Season-wide, growth of subyearlings was higher in the lower reach than in the upper reach of the Snake River.","language":"English","publisher":"Bonneville Power Administration","usgsCitation":"Perry, R., Hance, D., Plumb, J., Tiffan, K.F., Bickford, B., Benson, S.L., Rhodes, T., Brink, S., and Alcorn, B., 2024, Snake River Fall Chinook Salmon research and monitoring, v, 110 p.","productDescription":"v, 110 p.","ipdsId":"IP-159991","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":462763,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.cbfish.org"},{"id":462792,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Idaho, Oregon, Washington","otherGeospatial":"Snake River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": 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,{"id":70257354,"text":"70257354 - 2024 - Recent applications of the USGS National Crustal Model for Seismic Hazard Studies","interactions":[],"lastModifiedDate":"2024-08-15T15:19:06.555598","indexId":"70257354","displayToPublicDate":"2024-01-01T10:06:04","publicationYear":"2024","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Recent applications of the USGS National Crustal Model for Seismic Hazard Studies","docAbstract":"<p>The U.S. Geological Survey is developing the National Crustal Model (NCM) for seismic hazard studies to facilitate modeling site, path, and source components of seismic hazard across the conterminous United States. The NCM is composed of a 1km grid of geophysical profiles, extending from the Earth’s surface into the upper mantle. It is constructed from a threedimensional (3D) geologic framework and geophysical rules that use (1) a petrologic and mineral physics database; (2) a 3D temperature model; and (3) a calibrated rock type- and age-dependent porosity model. Parameters needed to estimate site response for existing ground motion models (GMMs), including the time-averaged velocity in the upper 30 meters (VS30), the depths to 1.0 and 2.5 km/s shear-wave velocity (Z1.0 and Z2.5), and sediment thickness, can be computed from the NCM. As GMMs continue to improve in the future, other metrics could also be extracted or derived from the NCM, such as fundamental period, site attenuation (ko), a fully frequency-dependent site response function, or 3D geophysical volumes for wavefield simulations. Application of the NCM may also benefit other aspects of seismic hazard analysis, including better accounting for path-dependent attenuation and geometric spreading, more accurate estimation of earthquake source properties such as hypocentral location and stress drop, and calculation of crustal strength profiles that inform estimates of the base of seismicity. </p>","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Geologic mapping forum 23/24 abstracts","largerWorkSubtype":{"id":12,"text":"Conference publication"},"language":"English","publisher":"Minnesota Geological Survey","usgsCitation":"Boyd, O.S., Smith, J.A., Moschetti, M.P., Aagaard, B.T., Graves, R., Hirakawa, E.T., and Ahdi, S.K., 2024, Recent applications of the USGS National Crustal Model for Seismic Hazard Studies, <i>in</i> Geologic mapping forum 23/24 abstracts, p. 60-61.","productDescription":"2 p.","startPage":"60","endPage":"61","ipdsId":"IP-164222","costCenters":[{"id":237,"text":"Earthquake Science 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,{"id":70261588,"text":"70261588 - 2024 - Investigating the atmospheric conditions associated with impactful shallow landslides in California (USA)","interactions":[],"lastModifiedDate":"2024-12-16T15:32:25.983624","indexId":"70261588","displayToPublicDate":"2024-01-01T09:26:44","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1421,"text":"Earth Interactions","active":true,"publicationSubtype":{"id":10}},"title":"Investigating the atmospheric conditions associated with impactful shallow landslides in California (USA)","docAbstract":"<p><span>Shallow landslides are often triggered during rainfall events, which can increase subsurface soil water pressure and destabilize hillslopes. The likelihood of regional shallow landslide initiation is often assessed through a comparison of rainfall intensity and duration to pre-established thresholds. While informative for landslide warning, this exclusive focus on rainfall exceeding thresholds does not consider the meteorological conditions producing the rainfall. Here, we ask the question, are there common meteorological characteristics that lead to landslide-triggering precipitation? We develop a catalog of 18 post-1995 widespread, impactful shallow landslide events occurring within 13 storms across California, USA, where initiation time could be constrained to a ≤6-h window. We examine storm characteristics during the landslide initiation window using atmospheric reanalysis products, radar observations, and quantitative precipitation estimates. We find that, while there are some common atmospheric characteristics across landslide events, they can occur under a range of atmospheric conditions. For example, all Northern California landslide events assessed are associated with moderate to strong atmospheric rivers (ARs), while Southern California landslides feature non-AR to strong AR conditions. The storm events evaluated herein share many characteristics of hydrologically important storms in California that did not necessarily result in landslides; thus, atmospheric characteristics alone may not be sufficient to determine whether landslides will occur. However, documenting the characteristics of landslide-triggering storms defines the conditions under which landslides tend to occur, provides analog events that can be useful in forecast applications, helps define future research directions relating to atmospheric conditions and landslides, and supports interdisciplinary research efforts.</span></p>","language":"English","publisher":"American Meteorological Society","doi":"10.1175/EI-D-24-0003.1","usgsCitation":"Oakley, N.S., Perkins, J.P., Bartlett, S.M., Collins, B.D., Comstock, K.H., Brien, D.L., Burgess, W., and Corbett, S.C., 2024, Investigating the atmospheric conditions associated with impactful shallow landslides in California (USA): Earth Interactions, v. 28, no. 1, e240003, 19 p., https://doi.org/10.1175/EI-D-24-0003.1.","productDescription":"e240003, 19 p.","ipdsId":"IP-157035","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":363,"text":"Landslide Hazards Program","active":false,"usgs":true}],"links":[{"id":467041,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1175/ei-d-24-0003.1","text":"Publisher Index Page"},{"id":465146,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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Weather and Water Extremes, Scripps Institute of Oceanography","active":true,"usgs":false}],"preferred":false,"id":921118,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Collins, Brian D. 0000-0003-4881-5359 bcollins@usgs.gov","orcid":"https://orcid.org/0000-0003-4881-5359","contributorId":149278,"corporation":false,"usgs":true,"family":"Collins","given":"Brian","email":"bcollins@usgs.gov","middleInitial":"D.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":921119,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Comstock, Karimah Halona 0009-0003-3662-5678","orcid":"https://orcid.org/0009-0003-3662-5678","contributorId":335639,"corporation":false,"usgs":true,"family":"Comstock","given":"Karimah","email":"","middleInitial":"Halona","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":921120,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brien, Dianne L. 0000-0003-3227-7963 dbrien@usgs.gov","orcid":"https://orcid.org/0000-0003-3227-7963","contributorId":229851,"corporation":false,"usgs":true,"family":"Brien","given":"Dianne","email":"dbrien@usgs.gov","middleInitial":"L.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":921121,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Burgess, W.P.","contributorId":347240,"corporation":false,"usgs":false,"family":"Burgess","given":"W.P.","email":"","affiliations":[{"id":83107,"text":"California Geological Survey, Sacramento","active":true,"usgs":false}],"preferred":false,"id":921122,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Corbett, Skye C. 0000-0003-3277-1021 scorbett@usgs.gov","orcid":"https://orcid.org/0000-0003-3277-1021","contributorId":200617,"corporation":false,"usgs":true,"family":"Corbett","given":"Skye","email":"scorbett@usgs.gov","middleInitial":"C.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":921123,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70250999,"text":"70250999 - 2024 - Watershed hydrology assessment for the Lower Colorado River Basin. Appendix D: RiverWare analyses","interactions":[],"lastModifiedDate":"2024-02-02T14:59:59.031674","indexId":"70250999","displayToPublicDate":"2024-01-01T08:50:09","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":1,"text":"Federal Government Series"},"seriesTitle":{"id":17147,"text":"Interagency Flood Risk Management Report","active":true,"publicationSubtype":{"id":1}},"title":"Watershed hydrology assessment for the Lower Colorado River Basin. Appendix D: RiverWare analyses","docAbstract":"<p>RiverWare is a river system modeling tool developed by CADSWES (Center of Advanced Decision Support for Water and Environmental Systems) that allows the user to simulate complex reservoir operations and perform period-of-record analyses for different scenarios. For the InFRM hydrology studies, RiverWare is used to generate a homogeneous regulated POR by simulating the basin as if the reservoirs and their current rule sets had been present in the basin for the entire time period. Statistical analyses can then be performed on the extended records at the gages. This report summarizes the RiverWare portion of the hydrologic analysis being completed for the InFRM Hydrology study of the Colorado River Basin.</p><p>The RiverWare model described in this chapter presents development of the Colorado River Basin hydrology, which mimics current operational conditions. The use of the RiverWare program allows for data extension to periods prior to dam construction. The utilization of longer gage record improves discharge frequency results and increases the confidence of the analysis being performed. The modeling evaluation criteria are: (1) evaluate output based on validating policies and functions, and (2) prioritize operation based on surcharge and flood control. A detailed explanation of the Colorado River Basin POR hydrology will be in a later section. </p><p>Calibration results will also be shown that illustrate the overall model performance for the POR. The time window simulation run is for January 01, 1930 – September 30, 2019. This time window captures all big events occurred over the Colorado River basin. Each simulated water year was inspected individually to better validate the results.</p><p>Historical pool elevations along with observed inflows and outflows were compared against the model simulated results.</p>","language":"English","publisher":"Interagency Flood Risk Management","collaboration":"USACE Fort Worth District, FEMA Region 6, NWS WGRFC","usgsCitation":"Wallace, D., and Watson, K.M., 2024, Watershed hydrology assessment for the Lower Colorado River Basin. Appendix D: RiverWare analyses: Interagency Flood Risk Management Report, 166 p.","productDescription":"166 p.","ipdsId":"IP-127610","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":424561,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://webapps.usgs.gov/infrm/"},{"id":425286,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","otherGeospatial":"Lower Colorado River","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -95.8,\n              28.65\n            ],\n            [\n              -95.8,\n              32\n            ],\n            [\n              -101,\n              32\n            ],\n            [\n              -101,\n              28.65\n            ],\n            [\n              -95.8,\n              28.65\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wallace, David 0000-0002-9134-8197","orcid":"https://orcid.org/0000-0002-9134-8197","contributorId":220786,"corporation":false,"usgs":true,"family":"Wallace","given":"David","email":"","affiliations":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"preferred":true,"id":892729,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Watson, Kara M. 0000-0002-2685-0260 kmwatson@usgs.gov","orcid":"https://orcid.org/0000-0002-2685-0260","contributorId":2134,"corporation":false,"usgs":true,"family":"Watson","given":"Kara","email":"kmwatson@usgs.gov","middleInitial":"M.","affiliations":[{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true},{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"preferred":true,"id":892730,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}}
,{"id":70259275,"text":"70259275 - 2024 - Ecology of Lake Erie - Chemistry, plankton & planktivory: A synthesis","interactions":[],"lastModifiedDate":"2024-10-03T13:31:43.005985","indexId":"70259275","displayToPublicDate":"2024-01-01T08:28:10","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":18728,"text":"Aquatic Ecosystem Health and Management","active":true,"publicationSubtype":{"id":10}},"title":"Ecology of Lake Erie - Chemistry, plankton & planktivory: A synthesis","docAbstract":"<p>As with other large lake ecosystems worldwide, Lake Erie can be considered a moving target for management, owing to physicochemical and biological changes brought on by anthropogenic change, both planned (e.g. nutrient and fisheries management) and unplanned (e.g. climate change, invasive species, modified land-use activities). These changes have challenged efforts to conserve biodiversity, sustain exploitable resources, and maintain the integrity of services valued by society both within the Lake Erie basin and (Fraker et al., 2022; Fussell et al., 2016; Sinclair et al., 2021; Sinclair et al., 2023) and outside of it (Allan et al., 2013; Jenny et al., 2020; Sterner et al., 2017). Some of these changes and their ramifications for management were documented in the first of four AEHM special issues devoted to the Lake Erie ecosystem (the fourth issue of 2023, volume 26, issue 4; see overview by Ludsin et al., 2023). That special issue focused explicitly on nutrient inputs and availability in Lake Erie and the lower food web, including planktonic and benthic microbial (including cyanobacteria), algal, and invasive dreissenid mussel communities. Similar to the previous Lake Erie special issue, this second one has focused on documenting the state of the lake, providing ecological understanding that could potentially benefit management. While some overlap in topics exists between issues, the studies conducted herein were completely independent of those previous investigations and offer unique insights. Specifically, the contributions to this current issue center on: 1) dynamics of water chemistry in Lake Erie’s central basin (i.e. bottom hypoxia; Ackerman et al., 2024) and western basin (i.e. mercury; Starr et al., 2024); 2) changes in primary producer biomass (Lesht et al., 2024), cyanotoxins (i.e. microcystin; Zastepa et al., 2024), and water quality (e.g. water clarity and dissolved nutrients; Howell et al., 2024); and 3) larval fish foraging (i.e. Lake Whitefish; Coregonus clupeaformis; Amidon et al., 2024) and community structure and phenology (DeBruyne et al., 2024). Below we summarize the major findings of these papers and offer a synthetic perspective on the value of this research for understanding the state of Lake Erie and enhancing management.</p>","language":"English","publisher":"Michigan State University Press","doi":"10.14321/aehm.027.01.116","usgsCitation":"Ludsin, S., Munawar, M., DeBruyne, R.L., Howell, E.T., Tyson, J., and Watkins, J.M., 2024, Ecology of Lake Erie - Chemistry, plankton & planktivory: A synthesis: Aquatic Ecosystem Health and Management, v. 27, no. 1, p. 116-124, https://doi.org/10.14321/aehm.027.01.116.","productDescription":"9 p.","startPage":"116","endPage":"124","ipdsId":"IP-163615","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":462529,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Lake Erie","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": 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-79.15795348814065,\n              42.52534601120243\n            ],\n            [\n              -79.00930804606551,\n              42.69563463834368\n            ],\n            [\n              -78.83853732874603,\n              42.76038448982763\n            ],\n            [\n              -78.84931646792415,\n              42.86956831700121\n            ],\n            [\n              -78.90437674926393,\n              42.88573072124271\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"27","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-01-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Ludsin, Stuart A.","contributorId":270532,"corporation":false,"usgs":false,"family":"Ludsin","given":"Stuart A.","affiliations":[{"id":36630,"text":"Ohio State University","active":true,"usgs":false}],"preferred":false,"id":914751,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Munawar, Mohiuddin","contributorId":344801,"corporation":false,"usgs":false,"family":"Munawar","given":"Mohiuddin","email":"","affiliations":[{"id":13015,"text":"Department of Fisheries and Oceans Canada","active":true,"usgs":false}],"preferred":false,"id":914752,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeBruyne, Robin L. 0000-0002-9232-7937 rdebruyne@usgs.gov","orcid":"https://orcid.org/0000-0002-9232-7937","contributorId":4936,"corporation":false,"usgs":true,"family":"DeBruyne","given":"Robin","email":"rdebruyne@usgs.gov","middleInitial":"L.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":914753,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Howell, E. Todd","contributorId":344802,"corporation":false,"usgs":false,"family":"Howell","given":"E.","email":"","middleInitial":"Todd","affiliations":[{"id":82411,"text":"Ontario Ministry of the Environment, Conservation, and Parks","active":true,"usgs":false}],"preferred":false,"id":914754,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tyson, Jeffrey","contributorId":344803,"corporation":false,"usgs":false,"family":"Tyson","given":"Jeffrey","affiliations":[{"id":7019,"text":"Great Lakes Fishery Commission","active":true,"usgs":false}],"preferred":false,"id":914755,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Watkins, James M.","contributorId":189286,"corporation":false,"usgs":false,"family":"Watkins","given":"James","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":914756,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}}
,{"id":70250215,"text":"70250215 - 2024 - Need and vision for global medium-resolution Landsat and Sentinel-2 data products","interactions":[],"lastModifiedDate":"2024-05-20T13:56:53.021648","indexId":"70250215","displayToPublicDate":"2024-01-01T06:39:13","publicationYear":"2024","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":"Need and vision for global medium-resolution Landsat and Sentinel-2 data products","docAbstract":"<p><span>Global changes in climate and land use are threatening natural ecosystems, biodiversity, and the ecosystem services people rely on. This is why it is necessary to track and monitor spatiotemporal change at a level of detail that can inform science, management, and&nbsp;policy development. The current constellation of multiple&nbsp;Landsat&nbsp;and Sentinel-2 satellites collecting imagery at predominantly&nbsp;</span><span class=\"math\"><span id=\"MathJax-Element-1-Frame\" class=\"MathJax_SVG\" data-mathml=\"<math xmlns=&quot;http://www.w3.org/1998/Math/MathML&quot;><mo is=&quot;true&quot;>&amp;#x2264;</mo></math>\"><span class=\"MJX_Assistive_MathML\">≤</span></span></span><span>30-m spatial resolution affords an opportunity for the generation of global medium- resolution products every few days. Our goal is to both identify the information needs and provide direction towards the generation of a suite of global, high-level, systematically-generated, medium-resolution products designed for both management and science. Our vision builds on the success of the NASA MODIS/VIIRS product suite, while recognizing the unique strengths of medium-resolution satellite data given their&nbsp;higher spatial resolution&nbsp;and longer time series. We propose a suite of 13 essential products that enable the characterization of the current state and changes in the biosphere,&nbsp;cryosphere, and&nbsp;hydrosphere, and would fill information needs identified by the Committee on Earth Observation Satellites for the Global Climate Observing System and the Global Terrestrial Observing System, by the National Research Council of the US National Academies in the decadal survey, and by others. These products are: land cover, land cover change, burned area, forest loss,&nbsp;vegetation indices,&nbsp;phenology, dynamic habitat indices,&nbsp;albedo, land surface temperature, snow cover, ice extent, surface water extent, and&nbsp;evapotranspiration. Furthermore, we provide a list of desirable products poised for addition to the essential products (e.g., crop type, emissivity, and ice sheet velocity). Lastly, we suggest aspirational products requiring further algorithm development (e.g., forest structure and crop yield). For the identified essential products, algorithms are in place, making it feasible to begin generating products systematically. These products should be accompanied by quality and accuracy assessments undertaken following consensus protocols. Five decades after the first&nbsp;Landsat satellite, and two decades after the&nbsp;MODIS&nbsp;products were first produced, it is time now for readily available, standardized, and consistent high-level products built upon medium-resolution imagery, thereby fulfilling the promise and the vision that inspired the Landsat program since its inception.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.rse.2023.113918","usgsCitation":"Radeloff, V., Roy, D., Wulder, M., Anderson, M., Cook, B., Crawford, C., Friedl, M., Gao, F., Gorelick, N., Hansen, M., Healey, S., Hostert, P., Hulley, G., Huntington, J., Johnson, D., Neigh, C., Lyapustin, A., Lymburner, L., Pahlevan, N., Pekel, J., Scambos, T.A., Schaaf, C., Strobl, P., Vermote, E., Woodcock, C., Zhang, H.K., and Zhu, Z., 2024, Need and vision for global medium-resolution Landsat and Sentinel-2 data products: Remote Sensing of Environment, v. 300, 113918, 26 p., https://doi.org/10.1016/j.rse.2023.113918.","productDescription":"113918, 26 p.","ipdsId":"IP-156892","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":440830,"rank":2,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.rse.2023.113918","text":"Publisher Index Page"},{"id":423036,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"300","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Radeloff, Volker C.","contributorId":296767,"corporation":false,"usgs":false,"family":"Radeloff","given":"Volker C.","affiliations":[{"id":18002,"text":"University of Wisconsin - Madison","active":true,"usgs":false}],"preferred":false,"id":888945,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roy, David P.","contributorId":294404,"corporation":false,"usgs":false,"family":"Roy","given":"David P.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":888946,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wulder, Mike","contributorId":330544,"corporation":false,"usgs":false,"family":"Wulder","given":"Mike","email":"","affiliations":[{"id":13540,"text":"Canadian Forest Service","active":true,"usgs":false}],"preferred":false,"id":888947,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Anderson, Martha","contributorId":269899,"corporation":false,"usgs":false,"family":"Anderson","given":"Martha","affiliations":[{"id":37009,"text":"USDA Agricultural Research Service","active":true,"usgs":false}],"preferred":false,"id":888948,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cook, Bruce D.","contributorId":294432,"corporation":false,"usgs":false,"family":"Cook","given":"Bruce 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Leo","contributorId":190978,"corporation":false,"usgs":false,"family":"Lymburner","given":"Leo","email":"","affiliations":[],"preferred":false,"id":888962,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Pahlevan, Nima","contributorId":328507,"corporation":false,"usgs":false,"family":"Pahlevan","given":"Nima","affiliations":[{"id":78385,"text":"NASA GSFC/ SSAI","active":true,"usgs":false}],"preferred":false,"id":888963,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Pekel, Jean-Francois","contributorId":294429,"corporation":false,"usgs":false,"family":"Pekel","given":"Jean-Francois","email":"","affiliations":[{"id":54481,"text":"European Commission","active":true,"usgs":false}],"preferred":false,"id":888964,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Scambos, Theodore 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Eric","contributorId":328512,"corporation":false,"usgs":false,"family":"Vermote","given":"Eric","affiliations":[{"id":39055,"text":"NASA GSFC","active":true,"usgs":false}],"preferred":false,"id":888968,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Woodcock, Curtis","contributorId":166666,"corporation":false,"usgs":false,"family":"Woodcock","given":"Curtis","affiliations":[{"id":13570,"text":"Boston University","active":true,"usgs":false}],"preferred":false,"id":888969,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Zhang, Hankui K.","contributorId":211965,"corporation":false,"usgs":false,"family":"Zhang","given":"Hankui","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":888970,"contributorType":{"id":1,"text":"Authors"},"rank":26},{"text":"Zhu, Zhe 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,{"id":70261197,"text":"70261197 - 2024 - Estimates of Southern White-tailed Ptarmigan daily nest survival from multiple sites in the Southern Rocky Mountains of Colorado","interactions":[],"lastModifiedDate":"2024-11-29T15:39:01.44456","indexId":"70261197","displayToPublicDate":"2024-01-01T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":947,"text":"Avian Conservation and Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Estimates of Southern White-tailed Ptarmigan daily nest survival from multiple sites in the Southern Rocky Mountains of Colorado","docAbstract":"<p>Estimating vital rates of avian species is important to understand population dynamics and develop potential conservation strategies that target rates for management. Avian species have reduced potential for high annual fecundity in alpine ecosystems due to a short breeding window and harsh weather conditions. We located nests from Southern White-tailed Ptarmigan (<i>Lagopus leucura altipetens</i>) across six study sites in the Southern Rocky Mountains of Colorado to estimate daily nest survival from 2013–2017. We used a known-fate hierarchical nest survival model and fit several covariates, including environmental conditions representing daily weather events and shrub cover, to describe variation in daily survival and derive estimates of nest success. We located and monitored 198 nests from 128 radio-marked ptarmigan hens. The mean nest success estimated as a derived parameter from daily nest survival was 45.6% (95% credible interval [CI]: 31.2–59.6%) and ranged from 40.3% to 50.3% across sites. Variation in daily nest survival was poorly described by the covariates we fit (95% CI of most slope coefficients overlapped 0), although there was some support for a negative effect of relative elevation (nests at lower elevations within a site survived at higher rates) and a positive effect of nest age (older nests survived at higher rates). We examined how variation in nest success was likely to influence the finite rate of population growth using a simple simulation with an age-transition matrix parameterized with previously reported fecundity and survival estimates. We found that the finite growth rate was predicted to increase 18.7% when evaluated from the lower to upper 95% CI estimated values of nest success, conditional on the other vital rates used in our simulation. We discuss the broader implications of these findings in the context of managing for nest survival of Southern White-tailed Ptarmigan.</p>","language":"English","publisher":"Resilience Alliance","doi":"10.5751/ACE-02566-190104","usgsCitation":"Wann, G.T., Seglund, A.E., Street, P.A., Parker, N.J., Nelson, S.L., Runge, J.P., Braun, C.E., and Aldridge, C.L., 2024, Estimates of Southern White-tailed Ptarmigan daily nest survival from multiple sites in the Southern Rocky Mountains of Colorado: Avian Conservation and Ecology, v. 19, no. 1, 4, 14 p., https://doi.org/10.5751/ACE-02566-190104.","productDescription":"4, 14 p.","ipdsId":"IP-153968","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":467042,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"http://dx.doi.org/10.5751/ace-02566-190104","text":"Publisher Index Page"},{"id":464593,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Southern Rocky Mountains","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -109.04311121117411,\n              41.012655687234144\n            ],\n            [\n              -109.04311121117411,\n              36.91934059975698\n            ],\n            [\n              -104.69133822114236,\n              36.91934059975698\n            ],\n            [\n              -104.69133822114236,\n              41.012655687234144\n            ],\n            [\n              -109.04311121117411,\n              41.012655687234144\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"19","issue":"1","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Wann, Gregory T. 0000-0001-9076-7819 wanng@usgs.gov","orcid":"https://orcid.org/0000-0001-9076-7819","contributorId":3855,"corporation":false,"usgs":true,"family":"Wann","given":"Gregory","email":"wanng@usgs.gov","middleInitial":"T.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":919595,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Seglund, Amy E.","contributorId":218686,"corporation":false,"usgs":false,"family":"Seglund","given":"Amy","email":"","middleInitial":"E.","affiliations":[{"id":39887,"text":"Colorado Parks and Wildlife","active":true,"usgs":false}],"preferred":false,"id":919596,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Street, Phillip A.","contributorId":346426,"corporation":false,"usgs":false,"family":"Street","given":"Phillip","email":"","middleInitial":"A.","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":919597,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Parker, Nicholas J.","contributorId":341574,"corporation":false,"usgs":false,"family":"Parker","given":"Nicholas","email":"","middleInitial":"J.","affiliations":[{"id":12661,"text":"Kansas State University","active":true,"usgs":false}],"preferred":false,"id":919598,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Nelson, Shelley L.","contributorId":346576,"corporation":false,"usgs":false,"family":"Nelson","given":"Shelley","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":919599,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Runge, Jonathan P.","contributorId":196756,"corporation":false,"usgs":false,"family":"Runge","given":"Jonathan","email":"","middleInitial":"P.","affiliations":[],"preferred":false,"id":919600,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Braun, Clait E.","contributorId":200013,"corporation":false,"usgs":false,"family":"Braun","given":"Clait","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":919601,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Aldridge, Cameron L. 0000-0003-3926-6941 aldridgec@usgs.gov","orcid":"https://orcid.org/0000-0003-3926-6941","contributorId":191773,"corporation":false,"usgs":true,"family":"Aldridge","given":"Cameron","email":"aldridgec@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":false,"id":919602,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}}
,{"id":70264095,"text":"70264095 - 2024 - Merging integrated population models and individual-based models to project population dynamics of recolonizing species","interactions":[],"lastModifiedDate":"2025-03-06T15:59:15.6884","indexId":"70264095","displayToPublicDate":"2024-01-01T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Merging integrated population models and individual-based models to project population dynamics of recolonizing species","docAbstract":"<p><span>Recolonizing species exhibit unique population dynamics, namely dispersal to and colonization of new areas, that have important implications for management. A resulting challenge is how to simultaneously model demographic and movement processes so that recolonizing species can be accurately projected over time and space. We introduce a framework for spatially explicit projection modeling that harnesses the rigorous parameter estimation made possible by an integrated population model (IPM) and the flexible movement modeling made possible by an individual-based model (IBM). Our framework has two components: [1] a Bayesian IPM-driven age- and state-structured population model that governs the population state process and estimation of demographic rates, and [2] an IBM-driven spatial model that allows for the projection of dispersal and habitat colonization. We applied this model framework to estimate current and project future dynamics of gray wolves&nbsp;(</span><i>Canis lupus</i><span>) in Washington State, USA. We used data from 74 telemetered wolves and yearly pup and pack counts to parameterize the model, and then projected statewide dynamics over 50 years. Mean population growth was 1.29 (95 % Bayesian Credible Interval = 1.26–1.33) during initial recolonization from 2009 to 2020 and decreased to 1.02 (95 % Prediction Interval = 0.98–1.04) in the projection period (2021–2070). Our results suggest that gray wolves have an ~100 % probability of colonizing the last of Washington State's three specified recovery regions by 2030, regardless of alternative assumptions about how dispersing wolves select new territories. Our spatially explicit projection model can be used to project the dynamics of any species for which spatial spread is an important driver of population dynamics.</span></p>","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2023.110340","usgsCitation":"Petracca, L., Gardner, B., Maletzke, B., and Converse, S.J., 2024, Merging integrated population models and individual-based models to project population dynamics of recolonizing species: Biological Conservation, v. 289, 110340, 14 p., https://doi.org/10.1016/j.biocon.2023.110340.","productDescription":"110340, 14 p.","ipdsId":"IP-151020","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":482973,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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,{"id":70263244,"text":"70263244 - 2024 - Individual-based ecological particle tracking model (ECO-PTM) for simulating juvenile chinook salmon migration and survival through the Sacramento – San Joaquin Delta","interactions":[],"lastModifiedDate":"2025-02-03T15:49:36.99796","indexId":"70263244","displayToPublicDate":"2024-01-01T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3331,"text":"San Francisco Estuary and Watershed Science","active":true,"publicationSubtype":{"id":10}},"title":"Individual-based ecological particle tracking model (ECO-PTM) for simulating juvenile chinook salmon migration and survival through the Sacramento – San Joaquin Delta","docAbstract":"Recovery of endangered salmon species in the Central Valley of California amidst prolonged drought and climate change necessitates innovative water management actions that balance species recovery and California's water demands. We describe an individual-based ecological particle tracking model (ECO-PTM) that can be used to assess the efficacy of proposed actions. Based on a random walk theory, the model tracks individual particles’ travel time, routing and survival in a flow field simulated by the Delta Simulation Model 2 hydrodynamic module (DSM2 HYDRO). The random walk particles are parameterized to have fish-like swimming behaviors, including upstream/downstream swimming, probabilistic holding behaviors, and stochastic swimming velocities. Particle routing at key junctions is based on well-established statistical models, and route-specific survival is calculated using the XT mean free-path length model. Behavioral parameters were estimated by fitting several competing models to a multiyear dataset of travel times from acoustic tagged juvenile salmon. The model’s baseline simulations under historical flow conditions from 1991 to 2016 successfully replicated essential relationships between salmon outmigration survival and hydrodynamic conditions, consistent with previous studies and the STARS (Survival Travel Time and Routing Simulation) statistical simulation model. Simulation results for management scenarios revealed multifaceted influences on fish survival, including Delta flow, flow at key junctions, route alterations, seasons, and water year characteristics. Importantly, these results highlight ECO-PTM’s potential to predict fish survival outcomes of proposed actions, serving as a foundation for informed future research, decision-making, and effective management strategies to enhance the survival prospects of out-migrating salmonids within the Sacramento-San Joaquin Delta ecosystem.","language":"English","publisher":"eScholarship","doi":"10.15447/sfews.2024v22iss4art4","usgsCitation":"Wang, X., Perry, R.W., Pope, A., Jackson, D., and Hance, D., 2024, Individual-based ecological particle tracking model (ECO-PTM) for simulating juvenile chinook salmon migration and survival through the Sacramento – San Joaquin Delta: San Francisco Estuary and Watershed Science, v. 22, no. 4, 4, 23 p., https://doi.org/10.15447/sfews.2024v22iss4art4.","productDescription":"4, 23 p.","ipdsId":"IP-163182","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":486828,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.15447/sfews.2024v22iss4art4","text":"Publisher Index Page"},{"id":481609,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento – San Joaquin Delta","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -121.56056300774354,\n              38.494229534007104\n            ],\n            [\n              -121.56056300774354,\n              37.898481687362576\n            ],\n            [\n              -121.08815089836878,\n              37.898481687362576\n            ],\n            [\n              -121.08815089836878,\n              38.494229534007104\n            ],\n            [\n              -121.56056300774354,\n              38.494229534007104\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"22","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-12-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Wang, Xiaochun","contributorId":225264,"corporation":false,"usgs":false,"family":"Wang","given":"Xiaochun","email":"","affiliations":[{"id":41085,"text":"California Department of Water Resources, Sacramento, CA, 95819","active":true,"usgs":false}],"preferred":false,"id":925998,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Perry, Russell W. 0000-0003-4110-8619 rperry@usgs.gov","orcid":"https://orcid.org/0000-0003-4110-8619","contributorId":2820,"corporation":false,"usgs":true,"family":"Perry","given":"Russell","email":"rperry@usgs.gov","middleInitial":"W.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":925999,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pope, Adam C. 0000-0002-7253-2247","orcid":"https://orcid.org/0000-0002-7253-2247","contributorId":223237,"corporation":false,"usgs":true,"family":"Pope","given":"Adam","middleInitial":"C.","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":926000,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jackson, Doug","contributorId":315556,"corporation":false,"usgs":false,"family":"Jackson","given":"Doug","email":"","affiliations":[{"id":68352,"text":"QEDA Consulting, LLC., 4007 Densmore Avenue N., Seattle, WA, 98103","active":true,"usgs":false}],"preferred":false,"id":926001,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hance, Dalton 0000-0002-4475-706X","orcid":"https://orcid.org/0000-0002-4475-706X","contributorId":220179,"corporation":false,"usgs":true,"family":"Hance","given":"Dalton","affiliations":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"preferred":true,"id":926002,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}}
,{"id":70266260,"text":"70266260 - 2024 - Longer holding times decrease dispersal but increase mortality of translocated scaled quail","interactions":[],"lastModifiedDate":"2025-05-02T16:48:52.098498","indexId":"70266260","displayToPublicDate":"2024-01-01T00:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Longer holding times decrease dispersal but increase mortality of translocated scaled quail","docAbstract":"<p><span>Scaled quail (</span><i>Callipepla squamata</i><span>) decline caused by habitat loss and fragmentation increased interest in translocation to reestablish populations. Yet factors determining translocation success are poorly understood. We tested hypotheses concerning the influence of source population and variation in delayed release strategy (1–9 weeks) on mortality and dispersal of wild-caught, translocated scaled quail. We trapped and translocated quail from 2016–2017 from source populations in the Edwards Plateau and Rolling Plains ecoregions to a large contiguous (&gt;40,000 ha) release site in Knox County, Texas, USA. We evaluated mortality and dispersal of translocated females as a function of source population, holding time prior to release, age, release location, and year using a multi-state mark-recapture model with state uncertainty. Scaled quail translocated within the Rolling Plains were more likely to exhibit philopatry to the release site. Quail with longer holding times had higher mortality but lower dispersal rates. The Edwards Plateau is a suitable source site for translocation in the Rolling Plains. The reduced dispersal but higher mortality of translocated scaled quail associated with longer holding times creates a decision tradeoff for managers.</span></p>","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.22498","usgsCitation":"Ruzicka, R., Rollins, D., Doherty, P.F., and Kendall, W.L., 2024, Longer holding times decrease dispersal but increase mortality of translocated scaled quail: Journal of Wildlife Management, v. 88, no. 1, e22498, 16 p., https://doi.org/10.1002/jwmg.22498.","productDescription":"e22498, 16 p.","ipdsId":"IP-151614","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":487938,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/jwmg.22498","text":"Publisher Index Page"},{"id":485349,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas","county":"Knox County","otherGeospatial":"South Wichita River Basin","geographicExtents":"{\n  \"type\": \"FeatureCollection\",\n  \"features\": [\n    {\n      \"type\": \"Feature\",\n      \"properties\": {},\n      \"geometry\": {\n        \"coordinates\": [\n          [\n            [\n              -103.2487789756043,\n              34.08946446033667\n            ],\n            [\n              -103.2487789756043,\n              31.880566287138876\n            ],\n            [\n              -100.09201762067653,\n              31.880566287138876\n            ],\n            [\n              -100.09201762067653,\n              34.08946446033667\n            ],\n            [\n              -103.2487789756043,\n              34.08946446033667\n            ]\n          ]\n        ],\n        \"type\": \"Polygon\"\n      }\n    }\n  ]\n}","volume":"88","issue":"1","noUsgsAuthors":false,"publicationDate":"2023-09-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Ruzicka, Rebekah E.","contributorId":354109,"corporation":false,"usgs":false,"family":"Ruzicka","given":"Rebekah E.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":935108,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rollins, Dale","contributorId":140708,"corporation":false,"usgs":false,"family":"Rollins","given":"Dale","email":"","affiliations":[],"preferred":false,"id":935109,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Doherty, Paul F. Jr.","contributorId":37636,"corporation":false,"usgs":false,"family":"Doherty","given":"Paul","suffix":"Jr.","email":"","middleInitial":"F.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":935110,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kendall, William L. 0000-0003-0084-9891","orcid":"https://orcid.org/0000-0003-0084-9891","contributorId":204844,"corporation":false,"usgs":true,"family":"Kendall","given":"William","email":"","middleInitial":"L.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":935111,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}}
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