The Eastern box turtle (Terrapene carolina carolina) population at the Jug Bay Wetlands Sanctuary, Lothian, MD has been monitored continuously for 29 years (1995-2023). We used open population capture-recapture models (Jolly-Seber) to estimate annual population size, survival probability, and recruitment rate. The model allows for unknown sex of individuals and includes information on individuals found dead. Our analysis documents a long-term decline of approximately 67% in box turtle population size at the Sanctuary over this nearly three-decade period. We estimate annual survival for both males and females, which does not show a systematic increase or decrease over time, averaging about 0.90 (95% CI: 0.86, 0.93) for females and 0.97 (95% CI: 0.94, 0.98) for males. Conversely, per-capita recruitment shows a marked decline over the first 15 years of the record, suggesting that population declines may be due to reduced recruitment. Conservation efforts for the species could benefit from a formal population viability analysis to understand the relative effects of survival and recruitment on changes in population size for this long-lived species.
","language":"English","publisher":"BioaRxiv","doi":"10.1101/2024.08.28.610102","usgsCitation":"Royle, A., Quinlan, M., and Swarth, C., 2024, Evidence for recruitment-mediated decline in an Eastern box turtle (Terrapene carolina carolina) population based on a 30-year capture-recapture data set from Maryland: BioRxiv, https://doi.org/10.1101/2024.08.28.610102.","productDescription":"22 p.","ipdsId":"IP-164958","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":466948,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1101/2024.08.28.610102","text":"External Repository"},{"id":465062,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Royle, J. Andrew 0000-0003-3135-2167 aroyle@usgs.gov","orcid":"https://orcid.org/0000-0003-3135-2167","contributorId":146229,"corporation":false,"usgs":true,"family":"Royle","given":"J. Andrew","email":"aroyle@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":920842,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Quinlan, Mike","contributorId":347120,"corporation":false,"usgs":false,"family":"Quinlan","given":"Mike","email":"","affiliations":[{"id":83074,"text":"Jug Bay Wetlands Sanctuary","active":true,"usgs":false}],"preferred":false,"id":920843,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Swarth, Christopher","contributorId":347121,"corporation":false,"usgs":false,"family":"Swarth","given":"Christopher","email":"","affiliations":[{"id":83075,"text":"no affiliations","active":true,"usgs":false}],"preferred":false,"id":920844,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70259258,"text":"70259258 - 2024 - From field station to forecast: Managing data at the Alaska Volcano Observatory","interactions":[],"lastModifiedDate":"2024-10-02T14:06:15.606438","indexId":"70259258","displayToPublicDate":"2024-08-28T08:56:46","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1109,"text":"Bulletin of Volcanology","active":true,"publicationSubtype":{"id":10}},"title":"From field station to forecast: Managing data at the Alaska Volcano Observatory","docAbstract":"The Alaska Volcano Observatory (AVO) uses multidisciplinary data to monitor and study dozens of active and potentially active volcanoes. Here, we provide an overview of internally and externally generated data types, tools and resources used in their management, and challenges faced. Data sources include the following: (1) a multiparameter (seismic, infrasound, GNSS, web cameras) ground-based monitoring network that spans 3000 km and transmits data in real time; (2) a variety of satellite-borne sensors that provide information about surface change and volcanic emissions; (3) geologic and gas field campaigns; and (4) other external data products that provide situation awareness. Each data type requires distinct acquisition, processing, storage, visualization, and archiving approaches. AVO uses a variety of externally and internally developed tools to handle individual data types as well as multidisciplinary volcanological data. A primary tool is the Geologic Database of Information on Volcanoes in Alaska (GeoDIVA), which stores detailed, searchable information on more than 140 volcanoes and over 1000 eruptions and unrest events, including images, eruption descriptions, and geologic station and sample data, metadata, and analyses. It interacts with other internal tools that store monitoring reports and other operational records. Additional data management resources used by AVO assist with alarms and alerts, state-of-health monitoring, and multiparameter visualization. Requirements for 24/7 accessibility, the ever-expanding portfolio of data, and transitioning new tools from development to operations are all challenges faced by AVO and other volcano observatories. AVO strives to meet FAIR data practices and ensure that data are available to national and international community efforts using external repositories as well as those hosted by AVO and its parent institutions.
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Geophysical Surveys","active":true,"usgs":false}],"preferred":false,"id":914590,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Kelly, Peter J. 0000-0002-3868-1046 pkelly@usgs.gov","orcid":"https://orcid.org/0000-0002-3868-1046","contributorId":5931,"corporation":false,"usgs":true,"family":"Kelly","given":"Peter","email":"pkelly@usgs.gov","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":914591,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Randall, Michael J. 0000-0001-7750-9612","orcid":"https://orcid.org/0000-0001-7750-9612","contributorId":44819,"corporation":false,"usgs":true,"family":"Randall","given":"Michael J.","affiliations":[],"preferred":false,"id":914592,"contributorType":{"id":1,"text":"Authors"},"rank":23}]}} ,{"id":70258798,"text":"70258798 - 2024 - Dynamic occupancy modelling of Asian elephants (Elephas maximus) reveals increasing landscape use in Nepal","interactions":[],"lastModifiedDate":"2024-09-26T13:48:12.795406","indexId":"70258798","displayToPublicDate":"2024-08-28T08:44:40","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}},"displayTitle":"Dynamic occupancy modelling of Asian elephants (Elephas maximus) reveals increasing landscape use in Nepal","title":"Dynamic occupancy modelling of Asian elephants (Elephas maximus) reveals increasing landscape use in Nepal","docAbstract":"Large mammals with general habitat needs can persist throughout mixed used landscapes, however, human-wildlife conflict frequently leads to their restriction to protected areas. Conservation efforts, especially for reducing conflicts with humans, can enhance tolerance of humans towards species like Asian elephants (Elephas maximus) in human-dominated landscapes. Here, we examine how elephant use in the Chure Terai Madhesh Landscape (CTML) covering the entire elephant range of Nepal changed between 2012 and 2020 in relationship to protection status and environmental conditions. We systematically surveyed ~ 42,000 km2 of potential habitat, by dividing the study area into 159 grid cells of 15 × 15 km2 and recorded elephant signs during the cool, dry season in three years (2012, 2018 and 2020). We analyzed the survey data in a single-species, multi-season (dynamic) occupancy modeling framework to test hypotheses regarding the influence of environmental conditions and protected area status on landscape use by elephants over time. The best-supported model included protected area effects on initial use, colonization, and detection probability as well as temporal variation in colonization and detection probability. Initial use and colonization rates were higher in protected areas, however elephants increasingly used cells located both inside and outside the protected areas, and the difference in use between protected areas and outside declined as elephants use became prevalent across most of the landscape. While elephant use was patchily distributed in the first year of surveys consistent with past descriptions of four sub-populations, elephant use consolidated into a western and eastern region in subsequent years with a gap in their distribution occurring between Chitwan and Bardiya National Parks. Our manuscript highlights the increasing landscape use by elephants in both protected areas and areas outside protected areas and suggests that management interventions that focus on reducing conflicts can promote greater use of both protected areas and areas outside of protected areas.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-024-70092-4","usgsCitation":"Ram, A.K., Lamichhane, B.R., Subedi, N., Yadav, N.K., Karki, A., Pandav, B., Brown, C., Khatri, T.B., and Yackulic, C., 2024, Dynamic occupancy modelling of Asian elephants (Elephas maximus) reveals increasing landscape use in Nepal: Scientific Reports, v. 14, 20023, 9 p., https://doi.org/10.1038/s41598-024-70092-4.","productDescription":"20023, 9 p.","ipdsId":"IP-160993","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":466949,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-024-70092-4","text":"Publisher Index Page"},{"id":462277,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Nepal","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[88.12044,27.87654],[88.04313,27.44582],[88.1748,26.81041],[88.06024,26.41462],[87.22747,26.3979],[86.02439,26.63098],[85.25178,26.7262],[84.67502,27.2349],[83.30425,27.36451],[81.99999,27.92548],[81.0572,28.4161],[80.08842,28.79447],[80.47672,29.72987],[81.11126,30.18348],[81.5258,30.42272],[82.32751,30.11527],[83.33712,29.46373],[83.89899,29.32023],[84.23458,28.83989],[85.01164,28.64277],[85.82332,28.20358],[86.95452,27.97426],[88.12044,27.87654]]]},\"properties\":{\"name\":\"Nepal\"}}]}","volume":"14","noUsgsAuthors":false,"publicationDate":"2024-08-28","publicationStatus":"PW","contributors":{"authors":[{"text":"Ram, Ashok Kumar","contributorId":344543,"corporation":false,"usgs":false,"family":"Ram","given":"Ashok","email":"","middleInitial":"Kumar","affiliations":[{"id":82383,"text":"Department of National Parks and Wildlife Conservation, Babarmahal, Kathmandu","active":true,"usgs":false}],"preferred":false,"id":914072,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Lamichhane, Babu Ram","contributorId":201694,"corporation":false,"usgs":false,"family":"Lamichhane","given":"Babu","email":"","middleInitial":"Ram","affiliations":[{"id":36232,"text":"National Trust for Nature Conservation, Khumaltar, POB 3712, Lalitpur, Nepal","active":true,"usgs":false}],"preferred":false,"id":914073,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Subedi, Naresh","contributorId":201695,"corporation":false,"usgs":false,"family":"Subedi","given":"Naresh","email":"","affiliations":[{"id":36232,"text":"National Trust for Nature Conservation, Khumaltar, POB 3712, Lalitpur, Nepal","active":true,"usgs":false}],"preferred":false,"id":914074,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yadav, Nabin Kumar","contributorId":344544,"corporation":false,"usgs":false,"family":"Yadav","given":"Nabin","email":"","middleInitial":"Kumar","affiliations":[{"id":82385,"text":"Ministry of Forests and Environment, Madhesh Pradesh, Janakpur, Nepal","active":true,"usgs":false}],"preferred":false,"id":914075,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Karki, Ajay","contributorId":344545,"corporation":false,"usgs":false,"family":"Karki","given":"Ajay","email":"","affiliations":[{"id":82383,"text":"Department of National Parks and Wildlife Conservation, Babarmahal, Kathmandu","active":true,"usgs":false}],"preferred":false,"id":914076,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pandav, Bivash","contributorId":344546,"corporation":false,"usgs":false,"family":"Pandav","given":"Bivash","email":"","affiliations":[{"id":82387,"text":"Wildlife Institute of India, Dehradun","active":true,"usgs":false}],"preferred":false,"id":914077,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Brown, Cory","contributorId":344547,"corporation":false,"usgs":false,"family":"Brown","given":"Cory","email":"","affiliations":[{"id":82388,"text":"US Fish and Wildlife Service, Washington D.C., USA","active":true,"usgs":false}],"preferred":false,"id":914078,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Khatri, Top B.","contributorId":344548,"corporation":false,"usgs":false,"family":"Khatri","given":"Top","email":"","middleInitial":"B.","affiliations":[{"id":82389,"text":"Ecosystem Based Adaptation Program (EBA) II, Kathmandu","active":true,"usgs":false}],"preferred":false,"id":914079,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Yackulic, Charles B. 0000-0001-9661-0724","orcid":"https://orcid.org/0000-0001-9661-0724","contributorId":218825,"corporation":false,"usgs":true,"family":"Yackulic","given":"Charles","middleInitial":"B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":914080,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70257859,"text":"70257859 - 2024 - Trees have similar growth responses to first-entry fires and reburns following long-term fire exclusion","interactions":[],"lastModifiedDate":"2024-08-29T12:21:04.445324","indexId":"70257859","displayToPublicDate":"2024-08-28T07:19:05","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1687,"text":"Forest Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"Trees have similar growth responses to first-entry fires and reburns following long-term fire exclusion","docAbstract":"Managing fire ignitions for resource benefit decreases fuel loads and reduces the risk of high-severity fire in fire-suppressed dry conifer forests. However, the reintroduction of low-severity wildfire can injure trees, which may decrease their growth after fire. Post-fire growth responses could change from first-entry fires to reburns, as first-entry fires reduce fuel loads and the vulnerability among trees to fire effects, which may result in trees sustaining less damage during reburns. To determine whether trees had growth responses that varied from first-entry fires to reburns, we cored 87 ponderosa pine trees in the Gila Wilderness, New Mexico, USA that experienced 3–5 fires between 1950 and 2012 following long-term fire-exclusion and 67 unburned control trees from the Gila and Apache-Sitgreaves National Forests. We assessed tree growth response to fire by comparing tree-ring growth among burned and unburned trees from two years before to two years after fires. We compared growth between burned and unburned trees using a bootstrapping procedure to calculate annual median tree-ring width index values with 95 % confidence intervals. We compared post-fire growth after first-entry fires and reburns following long-term fire-exclusion. Burned trees had similar growth responses following first-entry fires and reburns, with lower growth during the fire year through two years post-fire compared to unburned controls. Burned tree growth returned to expected rates following these immediate post-fire growth reductions. Interestingly, trees had lower growth during the year before and the year of reburns compared to the first-entry fire, reflecting greater aridity before reburns. Greater aridity may have contributed to larger-than-expected growth reductions following reburns, which could explain similar growth responses to first-entry fires and reburns. Our results indicate that trees had consistent short-term growth responses to low-severity fires following long-term fire-exclusion. As trees retained vigor after multiple fires, managing fires for resource benefit is an effective approach to reduce the likelihood of high-severity fire without long-term negative effects on tree growth.
Climate change in California is expected to alter future water availability, impacting water supplies needed to support future housing growth and agriculture demand. In groundwater-dependent regions like California's Central Coast, new land-use related water demand and decreasing recharge is already stressing depleted groundwater basins. We developed a spatially explicit state-and-transition simulation model that integrates climate, land-use change, water demand, and groundwater gain-loss to examine the impact of future climate and land use change on groundwater balance and water demand in five counties along the Central Coast from 2010 to 2060. The model incorporated downscaled groundwater recharge projections based on a Warm/Wet and a Hot/Dry climate future from a spatially explicit hydrological process-based model. Two urbanization projections from a parcel-based, regional urban growth model representing 1) recent historical and 2) state-mandated housing growth projections were used as alternative spatial targets for future urban growth. Agricultural projections were based on recent historical trends from remote sensing data. Annual projected changes in groundwater balance were calculated as the difference between land-use related water demand, based on historical estimates, and climate-driven recharge plus agriculture return flows. Results indicate that future changes in climate-driven groundwater recharge, coupled with cumulative increases in agricultural water demand, result in overall declines in future groundwater balance, with a Hot/Dry future resulting in cumulative groundwater decline in all but Santa Cruz County. Cumulative declines by 2060 are especially prominent in San Luis Obispo (−2.9 to −5.1 Bm3) and Monterey counties (−6.5 to −8.7 Bm3), despite limited changes in agricultural water demand over the model period. These two counties show declining groundwater reserves in a Warm/Wet future as well, while San Benito and Santa Barbara County barely reach equilibrium. These results suggest future groundwater supplies may not be able to keep pace with regional demand and declining climate-driven recharge, resulting in a potential reduction in water security in the region. However, our county-scale projections showed new housing and associated water demand does not conflict with California's groundwater sustainability goals. Rather, future climate coupled with increasing agricultural groundwater demand may reduce water security in some counties, potentially limiting available groundwater supplies for new housing.
Our aim was to determine the movement patterns of three abundant salmonids—Brown Trout Salmo trutta, Mountain Whitefish Prosopium williamsoni, and Rainbow Trout Oncorhynchus mykiss—in the Smith River watershed of Montana.
We tagged 7172 fish with passive integrated transponder (PIT) tags, monitored their movements past 15 stationary PIT arrays over 4 years, and located tagged fish between arrays by conducting mobile surveys.
Movement patterns varied seasonally, among species, and among locations. Movement was greatest in the middle portion of the watershed, which included a pristine main‐stem canyon and lower reaches of major tributaries. Fish rarely left the canyon, but movement into the canyon from other regions was common. Mountain Whitefish were most likely to move, and Brown Trout were least likely to move. Most fish travelled less than 10 km, but some fish travelled over 100 km. Distinct movement patterns were not evident; rather, a continuous spectrum of movement behaviors was apparent. Movements by Mountain Whitefish and Rainbow Trout increased during their spawning periods. Movements peaked when mean daily water temperatures were between 11.3 and 17.1°C.
Movements were diverse and probably contributed to metapopulation dynamics, population resiliency, and species diversity. Fish movements along stream networks connect populations across diverse landscapes, and therefore, protecting and restoring stream connectivity along inland streams such as the Smith River is critical to maintaining productive fish assemblages.
No abstract available.
","language":"English","publisher":"Arizona Geological Survey","usgsCitation":"Thompson, L.A., Haxel, G.B., Peterson, D.W., May, D.J., Tosdal, R., Miller, R.J., Gray, F., LeVeque, R., and Umhoefer, P.J., 2024, Bedrock geologic map of Organ Pipe Cactus National Monument and vicinity, southwest Arizona: Arizona Geological Survey Contributed Report CR-24-B, Report: 28 p.; 1 Plate: 78.00 x 53.73 inches.","productDescription":"Report: 28 p.; 1 Plate: 78.00 x 53.73 inches","ipdsId":"IP-167019","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":464915,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://repository.arizona.edu/handle/10150/674763"},{"id":464917,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Organ Pipe Cactus National Monument and vicinity","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -112.60466467320839,\n 31.818050523947505\n ],\n [\n -112.5712178771873,\n 31.858642501376792\n ],\n [\n -112.68111449268478,\n 32.21508023390702\n ],\n [\n -113.08725415865368,\n 32.22316494823238\n ],\n [\n -113.09681038608835,\n 31.972204964089357\n ],\n [\n -112.60466467320839,\n 31.818050523947505\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Thompson, Lisa A.","contributorId":347006,"corporation":false,"usgs":false,"family":"Thompson","given":"Lisa","email":"","middleInitial":"A.","affiliations":[{"id":34160,"text":"Arizona Geological Survey","active":true,"usgs":false}],"preferred":false,"id":920527,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Haxel, Gordon B. 0000-0002-6722-7803 gbhaxel@usgs.gov","orcid":"https://orcid.org/0000-0002-6722-7803","contributorId":261783,"corporation":false,"usgs":true,"family":"Haxel","given":"Gordon","email":"gbhaxel@usgs.gov","middleInitial":"B.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":920528,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Peterson, Donald W.","contributorId":347007,"corporation":false,"usgs":false,"family":"Peterson","given":"Donald","email":"","middleInitial":"W.","affiliations":[{"id":27990,"text":"Deceased","active":true,"usgs":false}],"preferred":false,"id":920529,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"May, Daniel J.","contributorId":347008,"corporation":false,"usgs":false,"family":"May","given":"Daniel","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":920530,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tosdal, Richard M.","contributorId":347010,"corporation":false,"usgs":false,"family":"Tosdal","given":"Richard M.","affiliations":[],"preferred":false,"id":920531,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Miller, Robert J.","contributorId":176277,"corporation":false,"usgs":false,"family":"Miller","given":"Robert","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":920532,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Gray, Floyd","contributorId":347011,"corporation":false,"usgs":false,"family":"Gray","given":"Floyd","affiliations":[],"preferred":false,"id":920533,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"LeVeque, Richard A.","contributorId":347012,"corporation":false,"usgs":false,"family":"LeVeque","given":"Richard A.","affiliations":[],"preferred":false,"id":920534,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Umhoefer, Paul J.","contributorId":200335,"corporation":false,"usgs":false,"family":"Umhoefer","given":"Paul","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":920535,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70257839,"text":"70257839 - 2024 - The extended Global Lake area, Climate, and Population (GLCP) dataset: Extending the GLCP to include ice, snow, and radiation-related climate variables","interactions":[],"lastModifiedDate":"2024-08-28T13:17:55.218007","indexId":"70257839","displayToPublicDate":"2024-08-27T08:12:09","publicationYear":"2024","noYear":false,"publicationType":{"id":27,"text":"Preprint"},"publicationSubtype":{"id":32,"text":"Preprint"},"seriesTitle":{"id":18346,"text":"EarthArXiv","active":true,"publicationSubtype":{"id":32}},"title":"The extended Global Lake area, Climate, and Population (GLCP) dataset: Extending the GLCP to include ice, snow, and radiation-related climate variables","docAbstract":"A changing climate and increasing human population necessitate understanding global freshwater availability. To enable assessment of lake water variability from local-to-global and monthly-to-decadal scales, we extended the Global Lake area, Climate, and Population (GLCP) dataset, which contains monthly lake surface area for 1.42 million lakes with paired basin-level climate and population data from 1995 through 2020. In comparison to the previous version of the GLCP, the extended version is monthly and includes information on lake ice cover as well as basin-level snow area, humidity, longwave and shortwave radiation, and cloud cover. The extended GLCP emphasizes FAIR data principles by expanding its scripting repository and maintaining unique HydroLAKES identifiers, which enables the GLCP to be joined with other HydroLAKES-derived products. Compared to the original version, the extended GLCP contains a richer suite of variables that enable disparate analyses of lake water trends at broad spatial and temporal scales.
","language":"English","publisher":"EarthArxiv","doi":"10.31223/X57X31","usgsCitation":"Meyer, M.F., Virdis, S.G., Yang, X., Brousil, M., McClure, R.P., Sharma, S., Woolway, R.I., Cramer, A.N., Ren, J., Katz, S.L., Hampton, S.E., and Shi, H., 2024, The extended Global Lake area, Climate, and Population (GLCP) dataset: Extending the GLCP to include ice, snow, and radiation-related climate variables: EarthArXiv, https://doi.org/10.31223/X57X31.","productDescription":"35 p.","ipdsId":"IP-167090","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":439189,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://dx.doi.org/10.31223/x57x31","text":"External Repository"},{"id":433245,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Meyer, Michael Frederick 0000-0002-8034-9434 mmeyer@usgs.gov","orcid":"https://orcid.org/0000-0002-8034-9434","contributorId":304191,"corporation":false,"usgs":true,"family":"Meyer","given":"Michael","email":"mmeyer@usgs.gov","middleInitial":"Frederick","affiliations":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"preferred":true,"id":911761,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Virdis, Salvatore G.P. 0000-0003-3927-9494","orcid":"https://orcid.org/0000-0003-3927-9494","contributorId":334733,"corporation":false,"usgs":false,"family":"Virdis","given":"Salvatore","email":"","middleInitial":"G.P.","affiliations":[{"id":80222,"text":"Asian Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":911762,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yang, Xiao 0000-0002-0046-832X","orcid":"https://orcid.org/0000-0002-0046-832X","contributorId":268230,"corporation":false,"usgs":false,"family":"Yang","given":"Xiao","email":"","affiliations":[{"id":55603,"text":"University of North Carolina Chapel Hill","active":true,"usgs":false}],"preferred":false,"id":911763,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brousil, Mattew R. 0000-0001-8229-9445","orcid":"https://orcid.org/0000-0001-8229-9445","contributorId":334731,"corporation":false,"usgs":false,"family":"Brousil","given":"Mattew R.","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":911764,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McClure, Ryan P. 0000-0001-6370-3852","orcid":"https://orcid.org/0000-0001-6370-3852","contributorId":268224,"corporation":false,"usgs":false,"family":"McClure","given":"Ryan","email":"","middleInitial":"P.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":911765,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Sharma, Sapna","contributorId":150332,"corporation":false,"usgs":false,"family":"Sharma","given":"Sapna","email":"","affiliations":[{"id":16184,"text":"York University","active":true,"usgs":false}],"preferred":false,"id":911766,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Woolway, R. Iestyn 0000-0003-0498-7968","orcid":"https://orcid.org/0000-0003-0498-7968","contributorId":297333,"corporation":false,"usgs":false,"family":"Woolway","given":"R.","email":"","middleInitial":"Iestyn","affiliations":[{"id":64373,"text":"European Space Agency Climate Office","active":true,"usgs":false}],"preferred":false,"id":911767,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cramer, Alli N. 0000-0002-0356-5782","orcid":"https://orcid.org/0000-0002-0356-5782","contributorId":268216,"corporation":false,"usgs":false,"family":"Cramer","given":"Alli","email":"","middleInitial":"N.","affiliations":[{"id":27155,"text":"University of California Santa Cruz","active":true,"usgs":false}],"preferred":false,"id":911768,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ren, Jianning 0000-0002-5849-2189","orcid":"https://orcid.org/0000-0002-5849-2189","contributorId":304196,"corporation":false,"usgs":false,"family":"Ren","given":"Jianning","email":"","affiliations":[{"id":16704,"text":"University of Nevada - Reno","active":true,"usgs":false}],"preferred":false,"id":911769,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Katz, Stephen L.","contributorId":245617,"corporation":false,"usgs":false,"family":"Katz","given":"Stephen","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":911770,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Hampton, Stephanie E.","contributorId":178718,"corporation":false,"usgs":false,"family":"Hampton","given":"Stephanie","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":911771,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Shi, Haoran 0000-0001-9543-3324","orcid":"https://orcid.org/0000-0001-9543-3324","contributorId":343708,"corporation":false,"usgs":false,"family":"Shi","given":"Haoran","email":"","affiliations":[{"id":36207,"text":"Bangor University","active":true,"usgs":false}],"preferred":false,"id":911772,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70257818,"text":"70257818 - 2024 - Reference 1D seismic velocity models for volcano monitoring and imaging: Methods, models, and applications","interactions":[],"lastModifiedDate":"2024-09-11T16:26:52.60017","indexId":"70257818","displayToPublicDate":"2024-08-27T07:07:37","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3372,"text":"Seismological Research Letters","onlineIssn":"1938-2057","printIssn":"0895-0695","active":true,"publicationSubtype":{"id":10}},"title":"Reference 1D seismic velocity models for volcano monitoring and imaging: Methods, models, and applications","docAbstract":"Seismic velocity models of the crust are an integral part of earthquake monitoring systems at volcanoes. 1D models that vary only in depth are typically used for real‐time hypocenter determination and serve as critical reference models for detailed 3D imaging studies and geomechanical modeling. Such models are usually computed using seismic tomographic methods that rely on P‐ and S‐wave arrival‐time picks from numerous earthquakes recorded at receivers around the volcano. Traditional linearized tomographic methods that jointly invert for source locations, velocity structure, and station corrections depend critically on having reasonable starting values for the unknown parameters, are susceptible to local misfit minima and divergence, and often do not provide adequate uncertainty information. These issues are often exacerbated by sparse seismic networks, inadequate distributions of seismicity, and/or poor data quality common at volcanoes. In contrast, modern probabilistic global search methods avoid these issues only at the cost of increased computation time. In this article, we review both approaches and present example applications and comparisons at several volcanoes in the United States, including Mount Hood (Oregon), Mount St. Helens (Washington), the Island of Hawai’i, and Mount Cleveland (Alaska). We provide guidance on the proper usage of these methods as relevant to challenges specific to volcano monitoring and imaging. Finally, we survey‐published 1D P‐wave velocity models from around the world and use them to derive a generic stratovolcano velocity model, which serves as a useful reference model for comparison and when local velocity information is sparse.
Seattle Public Utilities (SPU) has a history of conducting traditional fish surveys in urban streams of Seattle, Washington. Limited staff resources have reduced SPU's capacity to monitor fish, and environmental DNA (eDNA) was recognized as an alternative survey method that could potentially improve the efficiency and capacity of SPU-sponsored fish surveys. We performed spatiotemporal surveys of eDNA to assess occupancy and distribution of Chinook Salmon (Oncorhynchus tshawytscha), Coho Salmon (O. kisutch), and Coastal Cutthroat Trout (O. clarkii clarkii) in Thornton Creek, Seattle, between October 2018 and December 2020. Peak Chinook and Coho eDNA detections occurred in October and October–November, respectively, coinciding with expected adult return time. Chinook and Coho eDNA was detected in May at the time when juveniles outmigrate through the Lake Washington basin. Coastal Cutthroat Trout eDNA was widespread and detected at high rates across seasons, reflecting their ubiquitous distribution. Results from multiscale occupancy modeling suggested that distance upstream affected site-level occupancy probabilities for adult Chinook, but not Coho. Model results also suggested that the probability of Coho and Chinook eDNA occurring in water samples was affected by survey year. Finally, model results suggested that the probability of detecting Chinook eDNA in PCR technical replicates was affected by survey year and collection day but detection of Coho eDNA was only affected by collection day. This study indicates eDNA surveys are effective for assessing distribution and occupancy of salmonids in Seattle's urban streams. Integrating eDNA surveys into urban stream monitoring programs can help alleviate the burden of limited assets facing many resource managers.
Fire facilitates erosion through changes in vegetation and soil, with major postfire erosion commonly occurring even with moderate rainfall. As climate warms, the western United States (U.S.) is experiencing an intensifying fire regime and increasing frequency of extreme rain. We evaluated whether these hydroclimatic changes are evident in patterns of postfire erosion by modeling hillslope erosion following all wildfires larger than 100 km2 in California from 1984 to 2021. Our results show that annual statewide postfire hillslope erosion has increased significantly over time. To supplement the hillslope erosion modeling, we compiled modeled and measured postfire debris-flow volumes. We find that, in northern California, more than 50% of fires triggering the top 20 values of sediment mass and sediment yield occurred in the most recent decade (between 2011 and 2021). In southern California, the postfire sediment budget was dominated by debris flows, which showed no temporal trend. Our analysis reveals that 57% of postfire sediment erosion statewide occurred upstream of reservoirs, indicating potential impacts to reservoir storage capacity and thus increased risk to water-resource security with ongoing climate change.
Long-term, large-scale monitoring of wildlife populations is an integral part of conservation research and management. However, some traditional monitoring protocols lack the information needed to account for sources of measurement error in data analyses. Ignoring measurement error, such as partial availability, imperfect detection, and species misidentification, can lead to mischaracterizations of population states and processes. Accounting for measurement error is key to robust monitoring of populations, which can inform a wide variety of decisions, including harvest, habitat restoration, and determination of the legal status of species. We undertook an effort to retroactively minimize bias in a large-scale, long-term monitoring program for marine birds in the Salish Sea, Washington, USA, by conducting an auxiliary study to jointly estimate components of measurement error. We built a novel model in a Bayesian framework that simultaneously harnessed human observer and photographic data types to produce estimates necessary to correct for the effects of partial availability, imperfect detection, and species misidentification. Across all 31 species identified in photographs, both observers had instances of undercounting and overcounting birds but tended to undercount (observers undercounted totals across all species on 69.3%–78.9% of transects). We estimated species-specific correction factors that can be used to correct both historical and future counts from the Salish Sea survey, which has been running since 1992. Our novel modeling framework can be applied in other multispecies monitoring contexts where minimal photographic data can be collected for the purposes of correcting for measurement error in large-scale, long-term datasets.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.4961","usgsCitation":"Brusa, J., Farr, M., Evenson, J., Silverman, E., Murphie, B., Cyra, T., Tschaekofske, H., Spragens, K., and Converse, S.J., 2024, Correcting for measurement errors in a long-term aerial survey with auxiliary photographic data: Ecosphere, v. 15, no. 8, e4961, 15 p., https://doi.org/10.1002/ecs2.4961.","productDescription":"e4961, 15 p.","ipdsId":"IP-157545","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":487744,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.4961","text":"Publisher Index Page"},{"id":482967,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington","otherGeospatial":"Salish Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -123.31135582023572,\n 49.0056879632715\n ],\n [\n -123.31135582023572,\n 48.746723285327334\n ],\n [\n -122.69212389645517,\n 48.746723285327334\n ],\n [\n -122.69212389645517,\n 49.0056879632715\n ],\n [\n -123.31135582023572,\n 49.0056879632715\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"15","issue":"8","noUsgsAuthors":false,"publicationDate":"2024-08-27","publicationStatus":"PW","contributors":{"authors":[{"text":"Brusa, Jamie L.","contributorId":351922,"corporation":false,"usgs":false,"family":"Brusa","given":"Jamie L.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":929751,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Farr, Matthew T.","contributorId":351923,"corporation":false,"usgs":false,"family":"Farr","given":"Matthew T.","affiliations":[{"id":6934,"text":"University of Washington","active":true,"usgs":false}],"preferred":false,"id":929752,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Evenson, Joseph","contributorId":351924,"corporation":false,"usgs":false,"family":"Evenson","given":"Joseph","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":929753,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Silverman, Emily","contributorId":351925,"corporation":false,"usgs":false,"family":"Silverman","given":"Emily","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":929754,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Murphie, Bryan","contributorId":351927,"corporation":false,"usgs":false,"family":"Murphie","given":"Bryan","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":929755,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cyra, Thomas A.","contributorId":351929,"corporation":false,"usgs":false,"family":"Cyra","given":"Thomas A.","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":929756,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Tschaekofske, Heather","contributorId":351931,"corporation":false,"usgs":false,"family":"Tschaekofske","given":"Heather","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":929757,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Spragens, Kyle A.","contributorId":351933,"corporation":false,"usgs":false,"family":"Spragens","given":"Kyle A.","affiliations":[{"id":12438,"text":"Washington Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":929758,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":929759,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70257684,"text":"fs20243028 - 2024 - Structured science syntheses to inform decision making on Federal public lands","interactions":[],"lastModifiedDate":"2024-08-27T14:26:52.31055","indexId":"fs20243028","displayToPublicDate":"2024-08-26T15:00:00","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2024-3028","displayTitle":"Structured Science Syntheses to Inform Decision Making on Federal Public Lands","title":"Structured science syntheses to inform decision making on Federal public lands","docAbstract":"The U.S. Geological Survey, Bureau of Land Management, and U.S. Fish and Wildlife Service partnered to develop a new type of science product: the structured science synthesis. Structured science syntheses are peer-reviewed reports that synthesize science information about a priority resource management issue on public lands. Structured science syntheses are developed explicitly to facilitate the application of science to decision making. Key characteristics of structured science syntheses include that they are coproduced with resource managers, developed using clear, repeatable methods and designed for ease of use. The syntheses include different types of science information needed for analyses completed under the National Environmental Policy Act.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20243028","programNote":"Prepared in cooperation with the Bureau of Land Management and the U.S. Fish and Wildlife Service","usgsCitation":"Dietrich, E.I., Carter, S.K., Rutherford, T.K., Gilbert, M.A., Haby, T.S., Johnston, A.N., Jordan, S.E., Kleist, N.J., Lehrter, R.J., Masters, E.H., Mengelt, C., Stoneburner, A.L., Teige, E.C., Tull, J.C., Whipple, S.E., and Wood, D.J.A., 2024, Structured science syntheses to inform decision making on Federal public lands: U.S. Geological Survey Fact Sheet 2024–3028, 4 p., https://doi.org/10.3133/fs20243028.","productDescription":"4 p.","onlineOnly":"N","ipdsId":"IP-158168","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":481,"text":"Northern Rocky Mountain Science Center","active":true,"usgs":true}],"links":[{"id":433195,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20243028/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2024-3028"},{"id":433171,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2024/3028/fs20243028.xml"},{"id":433170,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2024/3028/images"},{"id":433078,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2024/3028/coverthb.jpg"},{"id":433132,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20235132","text":"Effects of Culverts on Habitat Connectivity in Streams—A Science Synthesis to Inform National Environmental Policy Act Analyses"},{"id":433131,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sir20235114","text":"Effects of Noise from Oil and Gas Development on Ungulates and Small Mammals—A Science Synthesis to Inform National Environmental Policy Act Analyses"},{"id":433079,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2024/3028/fs20243028.pdf","text":"Report","size":"5.12 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2024-3028"}],"contact":"Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526-8118
The Coastal California Gnatcatcher (Polioptila californica californica: “gnatcatcher”) is a resident species restricted to coastal sage scrub habitat in southern California. Listed as federally threatened, the gnatcatcher is subject to multiple threats, including habitat loss, fragmentation, and degradation, particularly in association with the increasing frequency of large wildfires. The California Gnatcatcher is a focal species under several habitat conservation plans and is monitored to determine population trends and evaluate the success of the plans in protecting the species.
Historically, gnatcatcher monitoring has been limited in geographic scope and has used differing methodologies, limiting the extent to which findings can be generalized across larger spatial scales and multiple populations. In 2015, we completed the first of an intended series of surveys following a standardized protocol designed to address two broad objectives. First, we sought to determine gnatcatcher occupancy at the regional scale, including habitat from throughout the species’ range in southern California, as well as in two subregions: Orange County and San Diego County, to address specific management objectives within those jurisdictions. In addition, we collected vegetation data to better understand gnatcatcher habitat associations that affect occupancy. In a parallel objective, we evaluated the effect of fire on gnatcatchers and their habitat by comparing occupancy and vegetation characteristics across sites varying in the length of time since the last fire. Data collected in 2020 allowed us to expand the study to include analyses of colonization (sites unoccupied in one year and occupied the next) and extinction (sites occupied in one year but not the next).
In 2020, we surveyed 327 regional points and 180 subregional points each in Orange and San Diego Counties. In addition, we surveyed 95–106 points within 4 postfire categories based on the year of the last fire at each point: before or during 2002 (“unburned”), 2003–06, 2007–10, and 2011–14. We surveyed for gnatcatchers during three area searches centered on each point at 2-week intervals commencing in mid-March. Vegetation data were collected during May–June using a modified point-intercept method along fixed transects.
Shrub and tree cover at our plots was dominated by California sagebrush (Artemisia californica), California buckwheat (Eriogonum fasciculatum), laurel sumac (Malosma laurina), sage (including Salvia mellifera and S. leucophylla), and sunflowers (including Encelia californica, E. farinosa, and Bahiopsis laciniata); however, most of the vegetation at plots consisted of non-native grass and herbaceous plants, indicating a high level of disturbance associated with fire. We documented vegetation differences at the subregional scale indicative of differences in fire history: in Orange County, overall shrub/tree cover was higher and herbaceous cover lower than in San Diego, where three large fires had burned within 17 years of this study. Both woody and herbaceous cover increased between 2016 and 2020 at the regional and two subregional scales, likely a response to above-average precipitation during 2 years preceding the 2020 surveys. Herbaceous vegetation also increased at postfire points; however, woody vegetation cover changed little between 2016 and 2020.
We modeled the effects of vegetation and physical (elevation, distance to Pacific coast, slope) covariates on gnatcatcher occupancy, colonization, and extinction probabilities in the regional, subregional, and postfire datasets. Cover of California sagebrush was the strongest predictor of gnatcatcher occupancy and appeared in the top models for every dataset. California buckwheat was another strong positive predictor of gnatcatcher occupancy in all datasets. Cover of sunflowers was a positive predictor of occupancy in the Orange County subregion, and both sunflowers and sage were positive predictors of occupancy at postfire points. In contrast, laurel sumac was negatively related to gnatcatcher occupancy in postfire habitats, with occupancy unlikely when sumac exceeded 50 percent cover. Herbaceous vegetation, including invasive grass, negatively affected gnatcatcher occupancy regionwide.
Covariates that were strong positive predictors of occupancy were also positive predictors of colonization and (or) negative predictors of extinction, and vice versa. Outside of the positive effects of California sagebrush and California buckwheat, and the negative effect of total herbaceous cover, we identified few covariates influencing colonization. In contrast, we identified many more predictors of extinction, including cover of laurel sumac and grass, which increased extinction risk, and cover of California sagebrush, sunflowers, and bare ground, along with time since last fire, which reduced extinction risk.
We used our modelled estimates of colonization and extinction probabilities to derive occupancy in 2020 based on starting occupancy in 2016. Gnatcatcher occupancy increased in 2020 at regional and subregional scales and in unburned habitat, growing by 19–35 percent since 2016. Among burned sites, occupancy tripled from 2016 to 2020 at points burned during 2011–14 but was unchanged at the 2007–10 postfire points and declined by 28 percent at points burned in 2003–06. The slow recovery of the gnatcatcher population in this latter category, which makes up 16 percent of the suitable habitat in San Diego County, is a matter of conservation concern warranting further attention.
Collectively, our rangewide results reveal a widespread and long-term effect of wildfire on California Gnatcatcher habitat, particularly in San Diego County. These data provide a baseline from which future monitoring can be used to evaluate changes in habitat condition over time and to improve our understanding of the factors and processes influencing gnatcatcher occupancy.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20241015","collaboration":"Prepared in cooperation with the San Diego Association of Governments, Natural Communities Coalition, California Department of Fish and Wildlife, and U.S. Fish and Wildlife Service","programNote":"Ecosystems Mission Area—Species Management Research Program","usgsCitation":"Kus, B.E., Houston, A., and Preston, K.L., 2024, Occupancy dynamics of the Coastal California Gnatcatcher in southern California: U.S. Geological Survey Open-File Report 2024–1015, 34 p., https://doi.org/10.3133/ofr20241015.","productDescription":"Report: viii, 34 p.; Data Release","numberOfPages":"34","onlineOnly":"Y","ipdsId":"IP-156536","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":433034,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20241015/full"},{"id":433032,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2024/1015/ofr20241015.xml"},{"id":433031,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2024/1015/ofr20241015.pdf","text":"Report","size":"15 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":433029,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7PC30JX","text":"USGS Data Release","description":"Kus, B.E., and Houston, A., 2021, Rangewide occupancy and post-fire recovery of California gnatcatchers in southern California (ver 2.0, March 2023): U.S. Geological Survey data release, https://doi.org/10.5066/F7PC30JX.","linkHelpText":"Rangewide occupancy and post-fire recovery of California gnatcatchers in southern California (ver 2.0, March 2023)"},{"id":433033,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2024/1015/images"},{"id":433030,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2024/1015/covrthb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -119.95012019018054,\n 35.34010394860475\n ],\n [\n -119.95012019018054,\n 32.347823604041594\n ],\n [\n -116.28068659643058,\n 32.347823604041594\n ],\n [\n -116.28068659643058,\n 35.34010394860475\n ],\n [\n -119.95012019018054,\n 35.34010394860475\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Western Ecological Research Center
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Geologic cross section N–N′ is the sixth in a series of geologic cross sections constructed by the U.S. Geological Survey to document and improve understanding of the geologic framework and petroleum systems of the Appalachian basin. Cross section N–N′ provides a regional view of the structural and stratigraphic framework of the Appalachian basin in the Valley and Ridge province in western and central Alabama; it spans approximately 69 miles (mi) (111 kilometers [km]).
This geologic cross section is a companion to geologic cross sections E–E′, D–D′, C–C′, I–I′, and A–A′ that are located approximately 350 to 550 mi (563 to 885 km) to the northeast. Cross section N–N' complements earlier geologic cross sections through the Alabama part of the Appalachian basin. Although some of the other cross sections show more structural and stratigraphic detail, they are of more limited extent geographically and stratigraphically.
Cross section N–N′ contains information that is useful for evaluating energy resources in the Appalachian basin. Although the Appalachian basin petroleum systems are not shown on the cross section, many of their key elements (such as source rocks, reservoir rocks, seals, and traps) can be inferred from lithologic units, unconformities, and geologic structures shown on the cross section. Other aspects of petroleum systems (such as the timing of petroleum generation and petroleum migration pathways) may be evaluated by burial history, thermal history, and fluid flow models based on what is shown on the cross section. In addition, cross section N–N′ may be used as a reconnaissance tool to identify plausible geologic structures and strata for the subsurface storage of liquid waste or for the sequestration of carbon dioxide.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3524","usgsCitation":"Trippi, M.H., Coleman, J.L., and Ryder, R.T., 2024, Cross section N–N' through the Valley and Ridge province of the southern Appalachian basin, from Greene County, west-central Alabama, to Bibb County, central Alabama (ver. 1.1, November 2024): U.S. Geological Survey Scientific Investigations Map 3524, 1 sheet, 51-p. pamphlet, https://doi.org/10.3133/sim3524.","productDescription":"Report: viii, 51 p.; 1 Sheet: 71.83 x 37.31 inches","numberOfPages":"51","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-154937","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":463685,"rank":12,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sim/3524/versionHist.txt","size":"1.69 KB","linkFileType":{"id":2,"text":"txt"}},{"id":433016,"rank":11,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim2985","text":"Scientific Investigations Map 2985","linkHelpText":"- Geologic Cross Section E-E' through the Appalachian Basin from the Findlay Arch, Wood County, Ohio, to the Valley and Ridge Province, Pendleton County, West Virginia"},{"id":433020,"rank":7,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim3425","text":"Scientific Investigations Map 3425","linkHelpText":"- Geologic Cross Section A–A′ Through the Appalachian Basin from the Southern Margin of the Ontario Lowlands Province, Genesee County, Western New York, to the Valley and Ridge Province, Lycoming County, North-Central Pennsylvania"},{"id":432956,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sim/3524/images/"},{"id":432955,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sim/3524/sim3524.XML","linkFileType":{"id":8,"text":"xml"}},{"id":432957,"rank":4,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sim3524/full","text":"Pamphlet","linkFileType":{"id":5,"text":"html"}},{"id":497892,"rank":13,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117262.htm","linkFileType":{"id":5,"text":"html"}},{"id":432953,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3524/sim3524_pamphlet.pdf","text":"Pamphlet","size":"2.15 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":432952,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3524/coverthb.jpg"},{"id":433017,"rank":10,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim3067","text":"Scientific Investigations Map 3067","linkHelpText":"- Geologic Cross Section D-D' Through the Appalachian Basin from the Findlay Arch, Sandusky County, Ohio, to the Valley and Ridge Province, Hardy County, West Virginia"},{"id":433018,"rank":9,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim3172","text":"Scientific Investigations Map 3172","linkHelpText":"- Geologic cross section C-C' through the Appalachian basin from Erie County, north-central Ohio, to the Valley and Ridge province, Bedford County, south-central Pennsylvania"},{"id":433019,"rank":8,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/sim3343","text":"Scientific Investigations Map 3343","linkHelpText":"- Geologic Cross Section I–I′ Through the Appalachian Basin from the Eastern Margin of the Illinois Basin, Jefferson County, Kentucky, to the Valley and Ridge Province, Scott County, Virginia"},{"id":432954,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3524/sim3524_map.pdf","size":"4.03 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"Alabama","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -88.63427073659228,\n 32.37457003921131\n ],\n [\n -84.65721995534267,\n 32.37457003921131\n ],\n [\n -84.65721995534267,\n 35.006773294083345\n ],\n [\n -88.63427073659228,\n 35.006773294083345\n ],\n [\n -88.63427073659228,\n 32.37457003921131\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","edition":"Version 1.0: August 26, 2024; Version 1.1: November 5, 2024","contact":"Geology, Energy & Minerals Science Center
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This report presents a plan supported by the National Earthquake Hazards Reduction Program (NEHRP) to coordinate domestic and international post-earthquake investigations (herein called “the Plan”). Post-earthquake scientific and engineering investigations are undertaken to capture critical information to understand the causes and impacts of the event, lessons from which can substantially improve the Nation’s resilience after future earthquakes. NEHRP is the Federal Government’s coordinated nationwide program to reduce risks to life and property from earthquakes. The Plan describes the activation and coordination of the four designated NEHRP Agencies in the Federal Government: Federal Emergency Management Agency, National Institute of Standards and Technology, U.S. National Science Foundation, and U.S. Geological Survey (USGS). The Plan also describes coordination between NEHRP Agencies and other organizations that may participate in pre-event and post-earthquake investigations, including non-NEHRP Federal agencies; State, regional, local, Tribal, and territorial agencies; domestic nongovernmental organizations; academic institutions and affiliated organizations; private companies; foreign governmental agencies and nongovernmental organizations; and international organizations. The Plan delineates the coordination of NEHRP post-earthquake scientific and engineering investigations to document the direct, indirect, and cascading physical and societal impacts from fault rupture and ground shaking hazards and from secondary hazards such as landslides, liquefaction, and tsunamis. In addition, the Plan identifies pre-event activities necessary to ensure that post-earthquake investigations are executed effectively. The USGS is the lead NEHRP Agency for activating and coordinating NEHRP post-earthquake investigations and for implementing this Plan. The USGS also leads coordination of the NEHRP Agencies in completing the pre-event activities identified in the Plan. The Plan has new information and supersedes USGS Circular 1242, “The Plan to Coordinate NEHRP Post-Earthquake Investigations,” which was published in 2003. This second-generation Plan was developed with the assistance of the Applied Technology Council (ATC) of Redwood City, California, under USGS contract 140G0121P0309, ATC-155. A 17-member Project Review Panel provided guidance on plan development, and input was solicited from subject matter experts representing key stakeholder groups and from participants at a public workshop.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1542","isbn":"978-1-4113-4584-3","collaboration":"Prepared in collaboration with the Federal Emergency Management Agency, National Institute of Standards and Technology, and U.S. National Science Foundation","usgsCitation":"Poland, C., Bray, J.D., Johnson, L., Nikolaou, S., Rathje, E., and Sherrod, B., 2024, Plan to coordinate post-earthquake investigations supported by the National Earthquake Hazards Reduction Program (NEHRP): U.S. Geological Survey Circular 1542, 36 p., https://doi.org/10.3133/cir1542. 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U.S. Geological Survey
Mail Stop 905
12201 Sunrise Valley Drive
Reston, VA 20192
The Kansas River is an essential water resource that provides drinking water to more than 950,000 people in northeastern Kansas. Water suppliers that rely on the Kansas River as a water-supply source use physical and chemical water-treatment strategies to remove contaminants before distribution. Water District No. 1 of Johnson County, Kansas (WaterOne), is the largest water supplier in the State and uses the Kansas and Missouri Rivers as water-supply sources to provide drinking water to the Kansas City metropolitan area. WaterOne has been using ozone disinfection as a primary water-treatment strategy since the summer of 2020. Water suppliers that rely on ozone disinfection have become increasingly concerned with the presence of elevated dissolved bromide (the negatively charged form of bromine; hereafter referred to as “bromide”) concentrations in their water-supply source. Ozone disinfection of source water containing elevated concentrations of bromide can lead to the formation of bromate, a regulated disinfection byproduct and probable carcinogen. Real-time computations of bromide concentrations upstream from the WaterOne source-water intake in the Kansas River can be used to assist WaterOne with proactive adjustment of water-treatment strategies. These computations can also be used to advance understanding of hydrologic processes affecting ozone disinfection and formation of bromate.
This report documents the development of the surrogate-regression model that computes bromide concentrations in real time at De Soto, Kansas, and characterizes daily and monthly bromide concentrations at this location during the study period. The U.S. Geological Survey (USGS), in cooperation with WaterOne, collected specific conductance and discrete bromide sample data at the USGS streamgage Kansas River at De Soto, Kans. (06892350; hereafter referred to as “De Soto”), during January 2021 through October 2023 to develop a surrogate-regression model using ordinary least-squares regression that computes bromide concentrations at De Soto, which is about 15 miles upstream from the WaterOne source-water intake in the Kansas River. Specific conductance explained about 85 percent of the variance in bromide concentrations at De Soto during the study period. The surrogate-regression model documented in this report estimated that bromide concentrations at De Soto were likely to exceed the WaterOne water-treatment level of concern (150 micrograms per liter [μg/L]) when specific conductance was greater than or equal to about 930 microsiemens per centimeter at 25 degrees Celsius. Surrogate-regression model computations of bromide concentrations documented in this report are available at the USGS National Real-Time Water-Quality website (https://nrtwq.usgs.gov/).
Bromide concentrations in discrete samples ranged from 31.9 to 251 μg/L and exceeded the water-treatment level of concern in about 34 percent of the 41 samples collected at De Soto during January 2021 through October 2023. Computed daily bromide concentrations ranged from 38.2 to 277 μg/L and exceeded the water-treatment level of concern about 46 percent of the time during January 2021 through October 2023. Generally, an inverse relation was observed between bromide and streamflow during the study period. Higher bromide concentrations were observed during September through February, and lower bromide concentrations were observed during March through August. Seasonal median bromide concentrations were significantly different in all pairwise seasonal combinations, except for summer versus spring. Computed median bromide concentrations were highest during winter, followed by fall, then spring and summer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20245078","collaboration":"Prepared in cooperation with the Water District No. 1 of Johnson County, Kansas","usgsCitation":"Williams, T.J., and Totzke, G.S., 2024, Computation of bromide concentrations at the Kansas River at De Soto, Kansas, January 2021 through October 2023: U.S. Geological Survey Scientific Investigations Report 2024–5078, 18 p., https://doi.org/10.3133/sir20245078.","productDescription":"Report: vii, 18 p.; Appendix; Dataset","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-166673","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":433130,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20245078/full"},{"id":433128,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2024/5078/downloads/","text":"Appendix 1","linkHelpText":"—Model Archival Summary for Bromide Concentration at U.S. Geological Survey Streamgage 06892350, Kansas River at De Soto, Kansas, during January 2021 through October 2023"},{"id":499481,"rank":8,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_117306.htm","linkFileType":{"id":5,"text":"html"}},{"id":433124,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2024/5078/coverthb.jpg"},{"id":433127,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2024/5078/images/"},{"id":433126,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2024/5078/sir20245078.XML"},{"id":433125,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2024/5078/sir20245078.pdf","text":"Report","size":"2.65 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2024–5078"},{"id":433129,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"}],"country":"United States","state":"Kansas","city":"De Soto","otherGeospatial":"Kansas River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -96.89522863083639,\n 39.3821856985449\n ],\n [\n -96.89522863083639,\n 38.76718861844998\n ],\n [\n -94.64788021564547,\n 38.76718861844998\n ],\n [\n -94.64788021564547,\n 39.3821856985449\n ],\n [\n -96.89522863083639,\n 39.3821856985449\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Kansas Water Science Center
U.S. Geological Survey
1217 Biltmore Drive
Lawrence, KS 66049
Climate change poses a significant threat to Arctic ecosystems. Evaluation of genetic diversity within and differentiation among populations is needed to effectively conserve Arctic species and ensure genetic variation is appropriately managed.This research examined the population genetic structure in Larus hyperboreus (Glaucous Gull), a circumpolar Arctic species that is declining in parts of its range. Population genetic information is needed to help delineate management units, including information on genetic differences among related species, among subspecies (currently distinguished by subtle morphometric and plumage differences), and among colonies in North America. We conducted double-digest restriction site-associated DNA sequencing for 62 L. hyperboreus, 18 L. argentatus smithsonianus (American Herring Gull), 6 L. a. argentatus (European Herring Gull), and 15 L. glaucescens (Glaucous-winged Gull) sampled across the Canadian and European Arctic. Interspecific analyses using 2,145 loci were unable to distinguish between all members of this species complex. Despite the geographic distance among sampling locations, molecular assignments and principal coordinates analyses based on 621 loci uncovered only weak population genetic differentiation among sampled European and Canadian colonies of L. hyperboreus. As L. hyperboreus occupying the eastern Canadian Arctic appears to be acting as a single panmictic population, conservation plans that protect Arctic habitat may help slow or reverse population declines. Proactive conservation strategies will benefit both L. hyperboreus and associated coastal Arctic ecosystems.
","language":"English","publisher":"BioOne","doi":"10.1093/ornithapp/duae037","usgsCitation":"Linklater, E., Sonsthagen, S.A., Robertson, G., Colston-Nepali, L., Vigfusdottir, F., and Friesen, V., 2024, 26 August 2024 Reduced representation sequencing reveals weak genetic differentiation between Canadian and European Larus hyperboreus (Glaucous Gull): Ornithological Applications, v. 126, no. 4, p. 1-11, https://doi.org/10.1093/ornithapp/duae037.","productDescription":"12 p.","startPage":"1","endPage":"11","ipdsId":"IP-154834","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":487657,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/ornithapp/duae037","text":"Publisher Index Page"},{"id":482264,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Iceland, United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -170.12197544996508,\n 62.606661683706506\n ],\n [\n -174.0470710526139,\n 50.852424322031254\n ],\n [\n -117.65103250372262,\n 49.497113822314844\n ],\n [\n -49.87838442995181,\n 44.44688847413198\n ],\n [\n -5.424596138025919,\n 65.78041162742718\n ],\n [\n -129.8047925349852,\n 79.78944673754077\n ],\n [\n -170.12197544996508,\n 62.606661683706506\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"126","issue":"4","noUsgsAuthors":false,"publicationDate":"2024-08-26","publicationStatus":"PW","contributors":{"authors":[{"text":"Linklater, Emma Lachance","contributorId":351063,"corporation":false,"usgs":false,"family":"Linklater","given":"Emma Lachance","affiliations":[{"id":34006,"text":"Queen’s University","active":true,"usgs":false}],"preferred":false,"id":927824,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sonsthagen, Sarah A. 0000-0001-6215-5874 ssonsthagen@usgs.gov","orcid":"https://orcid.org/0000-0001-6215-5874","contributorId":3711,"corporation":false,"usgs":true,"family":"Sonsthagen","given":"Sarah","email":"ssonsthagen@usgs.gov","middleInitial":"A.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":117,"text":"Alaska Science Center Biology WTEB","active":true,"usgs":true}],"preferred":true,"id":927825,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Robertson, Gregory J.","contributorId":351064,"corporation":false,"usgs":false,"family":"Robertson","given":"Gregory J.","affiliations":[{"id":83910,"text":"Wildlife Research Division, Environment and Climate Change Canada (ECCC)","active":true,"usgs":false}],"preferred":false,"id":927826,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Colston-Nepali, Lila","contributorId":351065,"corporation":false,"usgs":false,"family":"Colston-Nepali","given":"Lila","affiliations":[{"id":34006,"text":"Queen’s University","active":true,"usgs":false}],"preferred":false,"id":927827,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Vigfusdottir, Freydís","contributorId":351066,"corporation":false,"usgs":false,"family":"Vigfusdottir","given":"Freydís","affiliations":[{"id":36649,"text":"University of Iceland","active":true,"usgs":false}],"preferred":false,"id":927828,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Friesen, Vicki L.","contributorId":351067,"corporation":false,"usgs":false,"family":"Friesen","given":"Vicki L.","affiliations":[{"id":34006,"text":"Queen’s University","active":true,"usgs":false}],"preferred":false,"id":927829,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70257809,"text":"70257809 - 2024 - Constraining mean landslide occurrence rates for non-temporal landslide inventories using high-resolution elevation data","interactions":[],"lastModifiedDate":"2024-08-28T12:06:30.07639","indexId":"70257809","displayToPublicDate":"2024-08-26T07:01:13","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5739,"text":"Journal of Geophysical Research: Earth Surface","onlineIssn":"2169-9011","active":true,"publicationSubtype":{"id":10}},"title":"Constraining mean landslide occurrence rates for non-temporal landslide inventories using high-resolution elevation data","docAbstract":"Constraining landslide occurrence rates can help to generate landslide hazard models that predict the spatial and temporal occurrence of landslides. However, most landslide inventories do not include any temporal data due to the difficulties of dating landslide deposits. Here we introduce a method for estimating the mean landslide occurrence rate of deep-seated rotational and translational slides derived solely from high-resolution (≤3 m) elevation data and globally available estimates of the diffusion coefficient for sediment flux. The method applies a linear diffusion model to the roughest landslide deposits until they reach a representative non-landslide roughness distribution. This estimates the time for a landslide deposit to be unrecognizable in high-resolution digital elevation data, which we term the mean lifetime of the landslide. Using the mean lifetime and number of landslides within an area of interest, we can estimate the mean occurrence rate of landslides over that domain. We validate this approach using a comprehensive temporal inventory of landslides in western Oregon created using age-roughness curves that are calibrated with high-resolution elevation data and radiocarbon data. We find good agreement between our diffusion method and the existing age-roughness-derived estimates, producing mean lifetimes of 4500 and 5200 years (4% difference), respectively. Hazard maps produced using the two methodologies generally agree, with the maximum differences in landslide probability reaching 0.1. Due to the relative abundance of high-resolution elevation data compared with age-dated landslides, our method could help constrain landslide occurrence rates in areas previously considered unfeasible.
Ozette Lake is an ~100-m-deep coastal lake located along the outer coast of the Olympic Peninsula (Washington, USA); it is situated above the locked portion of the northern Cascadia megathrust but also relatively isolated from active crustal faults and intraslab earthquakes. Here we present a suite of geophysical and geological evidence for earthquake-triggered mass transport deposits (MTDs) and related turbidite deposition in Ozette Lake since ca. 14 ka. Comprehensive high-resolution bathymetry data, seismic reflection profiles, and sediment cores are used to characterize the post-glacial stratigraphic framework and examine paleoseismic evidence in the lacustrine sediments. Stacked sequences of MTDs along the steep eastern flanks of the lake appear to grade basin-ward from thick, chaotic, blocky masses to thin, parallel-bedded turbidite beds. The discrete turbidite event layers are separated by fine-grained (silt and clay) lake sedimentation. The event layers are observed throughout the lake, but the physical characteristics of the deposits vary considerably depending on proximity to primary depocenters, steep slopes, and subaqueous deltas. A total of 30–34 event deposits are observed in the post-glacial record. Radiometric dating was used to reconstruct a detailed sedimentation history over the last ~5.5 k.y., develop an age model, and estimate the recurrence (365–405 yr) for the most recent 12 event layers. Based on sedimentological characteristics, temporal overlap with other regional paleoseismic chronologies, and recurrence estimates, at least 10 of the dated event layers appear to be sourced from slope failures triggered by intense shaking during megathrust ruptures; the recurrence interval for these 10 events is 440–560 yr. Thus, Ozette Lake contains one of the longest and most robust geological records of repeated shaking along the northern Cascadia subduction zone.
Cyclonic storms (i.e., hurricanes) are powerful disturbance events that often cause widespread forest damage. Storm-related canopy damage reduces rainfall interception and evapotranspiration, but impacts on streamflow regimes are poorly understood. We quantify streamflow changes in Puerto Rico following Hurricane Maria in September 2017, and evaluate whether forest cover and storm-related canopy damage account for the differences. Streams are particularly vulnerable to flooding in early post-disturbance stages during hurricane season, so we focus on 3 months (Oct–Dec) following the hurricane. To discern changes in rainfall responses, we partitioned streamflow into baseflow and quickflow using a digital filter. We collected 2010–2017 streamflow and rainfall data from 18 watersheds and compared the relative magnitude of post- to pre-hurricane double mass curve slopes of baseflow and quickflow volumes against rainfall. Several watersheds displayed higher post-hurricane quickflow and baseflow, however, the response was variable. The magnitude of quickflow increase was greater in watersheds with high forest damage. Under the same level of relative damage, watersheds with low initial forest cover had greater quickflow increases than highly forested ones. Conversely, baseflow generally increased, but increases were greater in highly forested watersheds and smaller in highly damaged watersheds. These results suggest that post-storm baseflow increases were due to recharge of hurricane-related rainfall, as well as forest transpiration interruption and soil disturbance enhancing recharge of post-hurricane rainfall, while increases to quickflow are related to loss of canopy rainfall interception and higher soil saturation decreasing infiltration. Our research demonstrates that forest damage from disturbance lowers quickflow and elevates baseflow in highly forested watersheds, and elevates quickflow and lowers baseflow in less-forested watersheds. Less-forested watersheds may be closer to the forest cover loss threshold needed to elicit a streamflow response following disturbance, suggesting higher flooding potential downstream, and a lower storm-related forest disturbance threshold than in heavily forested watersheds.
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Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Hyperspectral imaging predicts differences in carbon and nitrogen status among representative biocrust functional groups of the Colorado Plateau","docAbstract":"Biological soil crusts (biocrusts) are widespread soil photosynthetic communities covering about 12% of Earth's land surface and play crucial roles in terrestrial carbon (C) and nitrogen (N) cycles, yet scalable quantifications of biocrusts and their biogeochemical contributions are notably lacking. While remote sensing has enormous potential to assess, scale, and contextualize biocrusts and their functions, the applicability of hyperspectral data in predicting C- and N-related biocrust traits remains largely unexplored. We address this issue by evaluating the potential of in situ hyperspectral data to predict C and N across a range of biocrust species and different environmental conditions. We found that in situ hyperspectral reflectance measurements can be used to predict biocrust tissue C/N ratios and N concentrations with relatively high accuracy but to a lesser extent for potential biocrust N2 fixation rates. Critical wavelength domains included the visible region of the spectrum from roughly 490–600 nm, which most effectively captured variations in biocrust tissue C, and the shortwave infrared region from 1,150 to 1,350 nm and 1,550–1,650 nm, which most effectively captured biocrust tissue N and N2 fixation potential. Finally, we provide evidence that multi- and hyperspectral missions with targeted band placement, such as the proposed 26-band Landsat Next, could be effective in predicting biocrust traits. This work provides a critical step in understanding how to apply data from new and upcoming satellite missions to the monitoring of biocrusts.
","language":"English","publisher":"AGU","doi":"10.1029/2024JG008089","usgsCitation":"Yan, D., Reed, S., Rutherford, W., Javadian, M., Reibold, R.H., Villarreal, M.L., Poulter, B., Song, S., and Smith, W., 2024, Hyperspectral imaging predicts differences in carbon and nitrogen status among representative biocrust functional groups of the Colorado Plateau: Journal of Geophysical Research: Biogeosciences, v. 129, no. 8, e2024JG008089, 14 p., https://doi.org/10.1029/2024JG008089.","productDescription":"e2024JG008089, 14 p.","ipdsId":"IP-153887","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":499268,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2024jg008089","text":"Publisher Index 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However, few toxicity studies have been conducted for species of concern in Lake Champlain (NY and VT). We conducted 12-hour flow-through toxicity tests with 4 species of native mussels, 6 species of fish, and 1 amphibian species. All tests included exposure of invasive larval Petromyzon marinus (sea lamprey) and were conducted with concentrations that bracketed the predicted minimum lethal concentration required to control larval sea lamprey. Mussel species’ NOEC, LOEC, LC25, and LC50 values ranged from 1.33 to 2.12, 1.71–2.66, 1.75–3.05, and 2.03–4.84 times field determined LC99.9s for sea lamprey (×SLLC99.9) in TFM-only toxicity tests, and from 1.36 to 1.70, 1.68–2.03, 1.86–2.10, and 2.35–2.68 × SLLC99.9 for TFM:1%Nic toxicity tests, respectively. Fish species NOEC, LOEC, LC25, and LC50 values ranged from 0.60 to 1.89, 0.73–2.13, 0.72–2.11, and 0.76–2.18 × SLLC99.9 in TFM-only toxicity tests, and from 0.64 to 2.48, 0.85–3.10, 0.74–3.05, and 0.78–3.16 × SLLC99.9 for TFM:1%Nic toxicity tests, respectively. Amphibian species NOEC, LOEC, LC25, and LC50 values ranged from 0.74 to 0.75, 0.85–0.95, 0.83–0.87, and 0.85–0.91 × SLLC99.9 in TFM-only toxicity tests, and from 0.63 to 0.65, 0.80–0.88, 0.77–0.82, and 0.78–0.87 × SLLC99.9 for TFM:1%Nic toxicity tests, respectively. Generally, mussel species were tolerant, fish sensitivities were variable, and the amphibian species was sensitive to TFM and TFM:1%Nic.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jglr.2024.102422","usgsCitation":"Neuderfer, G.N., Durfey, L.E., Calloway, M.T., Smith, S.J., and Schueller, J., 2024, Acute toxicity of lampricides to non-target species of concern in the Lake Champlain watershed: Journal of Great Lakes Research, v. 50, no. 6, 102422, 9 p., https://doi.org/10.1016/j.jglr.2024.102422.","productDescription":"102422, 9 p.","ipdsId":"IP-160364","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":433491,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New York, Vermont","otherGeospatial":"Lake Champlain watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -73.64408912592127,\n 44.99762815971462\n ],\n [\n -73.64408912592127,\n 43.70578516904615\n ],\n [\n -72.80563760709894,\n 43.70578516904615\n ],\n [\n -72.80563760709894,\n 44.99762815971462\n ],\n [\n -73.64408912592127,\n 44.99762815971462\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"50","issue":"6","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Neuderfer, Gary N.","contributorId":343867,"corporation":false,"usgs":false,"family":"Neuderfer","given":"Gary","email":"","middleInitial":"N.","affiliations":[{"id":82230,"text":"New York State Department of Environmental Conservation, Albany, NY (Retired)","active":true,"usgs":false}],"preferred":false,"id":912233,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Durfey, Lance E.","contributorId":343868,"corporation":false,"usgs":false,"family":"Durfey","given":"Lance","email":"","middleInitial":"E.","affiliations":[{"id":82230,"text":"New York State Department of Environmental Conservation, Albany, NY (Retired)","active":true,"usgs":false}],"preferred":false,"id":912234,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Calloway, Michael T.","contributorId":343869,"corporation":false,"usgs":false,"family":"Calloway","given":"Michael","email":"","middleInitial":"T.","affiliations":[{"id":82233,"text":"Federal Energy Regulatory Commission, Washington, DC","active":true,"usgs":false}],"preferred":false,"id":912235,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Smith, Stephen J.","contributorId":38926,"corporation":false,"usgs":false,"family":"Smith","given":"Stephen","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":912236,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Schueller, Justin R. 0000-0002-7102-3889","orcid":"https://orcid.org/0000-0002-7102-3889","contributorId":213527,"corporation":false,"usgs":true,"family":"Schueller","given":"Justin","middleInitial":"R.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":912237,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ]}