Motile Aeromonad septicemia is a substantial concern during fish propagation and can be catastrophic for fish hatcheries. We tested the efficacy of two different drugs (florfenicol and oxytetracycline) offered with feed as possible treatment options to control mortality because of motile Aeromonad infection. We offered top-coated medicated feeds to hatchery-reared Sander vitreus (walleye) that were naturally infected with motile Aeromonad infection during two separate trials in 2011 and 2012. Substantial walleye mortality occurred before positive clinical outcomes from the medicated feed treatments were observed, and additional treatment measures were taken by hatchery staff to mitigate further mortality in their walleye production tanks. This report summarizes the data that were collected during medicated feed trials. Statistical inferences on treatment efficacy are not included because of the confounding treatments and possible secondary pathogens present throughout this study.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20231056","usgsCitation":"Merkes, C.M., Tuttle-Lau, M.T., Schleis, S.M., and Cupp, A.R., 2024, Summary of data collected during field efficacy trials of florfenicol and oxytetracycline dihydrate in controlling mortality in walleye (Sander vitreus) because of motile Aeromonad infections: U.S. Geological Survey Open-File Report 2023–1056, 19 p., https://doi.org/10.3133/ofr20231056.","productDescription":"Report: vii, 19 p.; Appendix; Data Release","numberOfPages":"32","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-141472","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":426453,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VHDBCW","text":"USGS data release","linkHelpText":"Trial data for field effectiveness of florfenicol and oxytetracycline dihydrate in controlling mortality in walleye (Sander vitreus)"},{"id":426454,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20231056/full"},{"id":426452,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2023/1056/downloads/","text":"Appendix 1","linkHelpText":"—Documentation and Data"},{"id":426451,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2023/1056/images/"},{"id":426448,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2023/1056/coverthb.jpg"},{"id":426449,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2023/1056/ofr20231056.pdf","text":"Report","size":"1.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2023–1056"},{"id":426450,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2023/1056/ofr20231056.XML"}],"contact":"Director, Upper Midwest Environmental Sciences Center
U.S. Geological Survey
2630 Fanta Reed Road
La Crosse, WI 54603
Discovered by Galileo Galilei more than 400 years ago and imaged in detail by the Voyager 2 Galileo spacecraft, Jupiter’s icy moon Europa has been a source of intrigue. A range of science investigations indicate that it contains the key ingredients for habitability, notably energy, chemistry, and liquid water. Europa’s surface is geologically complex and, based on the dearth of impact craters, interpreted to be as young as ~60 Ma. The array of geologic features that characterize the surface include extensive ridged plains, regions of broad disruption termed chaotic terrain, long, quasi-linear ridges that span thousands of kilometers, and bands as much as 60 kilometers wide and that extend 100s of kilometers. These features, along with other geophysical measurements, indicate the presence of a global briny liquid water ocean beneath the ice shell. It was not until the arrival of the Galileo spacecraft in 1995 that the true nature and level of complexity of the surface was revealed. Although image data returned by Galileo provided insight into the structure of a variety of regions, the entire satellite has yet to be observed at a regional scale (less than 250 meters per pixel) and the detailed geologic nature of much of its surface remains a mystery. Establishing the global context of the distribution and timing of Europan geologic units forms a basis to understand regional and local scale processes, serves as a tool for the planning of future missions, and most of all is essential to gaining insight into the potential habitability of this icy world.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3513","collaboration":"Prepared in cooperation with the National Aeronautics and Space Administration","usgsCitation":"Leonard, E.J., Patthoff, D.A., and Senske, D.A, 2024, Global geologic map of Europa: U.S. Geological Survey Scientific Investigations Map 3513, scale 1:15,000,000, pamphlet 18 p., https://doi.org/10.3133/sim3513.","productDescription":"Report: iv, 18 p.; Metadata: 2; Database; ReadMe; 1 Map: 53.61 × 28.32 inches","numberOfPages":"18","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-097718","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":435021,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P13ZCQK6","text":"USGS data release","linkHelpText":"Interactive Map: Global Geologic Map of Europa"},{"id":426471,"rank":7,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3513/sim3513_sheet.pdf","text":"Map Sheet","size":"12 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":426470,"rank":6,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3513/sim3513_readme.txt","size":"5 KB","linkFileType":{"id":2,"text":"txt"}},{"id":426465,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3513/covrthb.jpg"},{"id":426469,"rank":5,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3513/sim3513_metadata.xml","size":"5 KB","linkFileType":{"id":8,"text":"xml"}},{"id":426466,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3513/sim3513_pamphlet.pdf","text":"Pamphlet","size":"2 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":426467,"rank":3,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3513/sim3513_gis.zip","text":"Europa GIS","size":"150 MB","linkFileType":{"id":6,"text":"zip"}},{"id":426468,"rank":4,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3513/sim3513_metadata.txt","size":"10 KB","linkFileType":{"id":2,"text":"txt"}}],"contact":"Astrogeology Science Center
U.S. Geological Survey
2255 N. Gemini Dr.
Flagstaff, AZ 86001
Artificial manipulation of lake water levels through practices like winter water-level drawdown (WD) is prevalent across many regions, but the spatiotemporal patterns are not well documented due to limited in situ monitoring. Multi-sensor satellite remote sensing provides an opportunity to map and analyze drawdown frequency and metrics (timing, magnitude, duration) at broad scales. This study developed a cloud computing framework to process time series of synthetic aperture radar (Sentinel 1-SAR) and optical sensor (Landsat 8, Sentinel 2) data to characterize WD in 166 lakes across Massachusetts, USA, during 2016–2021. Comparisons with in situ logger data showed that the Sentinel 1-derived surface water area captured relative water-level fluctuations indicative of WD. A machine learning approach classified lakes as WD versus non-WD based on seasonal water-level fluctuations derived from Sentinel 1-SAR data. The framework mapped WD lakes statewide, revealing prevalence throughout Massachusetts with interannual variability. Results showed WDs occurred in over 75% of lakes during the study period, with high interannual variability in the number of lakes conducting WD. Mean WD magnitude was highest in the wettest year (2018) but % lake area exposure did not show any association with precipitation and varied between 8% to 12% over the 5-year period. WD start date was later and duration was longer in wet years, indicating climate mediation of WD implementation driven by management decisions. The data and tools developed provide an objective information resource to evaluate ecological impacts and guide management of this prevalent but understudied phenomenon. Overall, the results and interactive web tool developed as part of this study provide new hydrologic intelligence to inform water management and policies related to WD practices.
","language":"English","publisher":"MDPI","doi":"10.3390/rs16060947","usgsCitation":"Kumar, A., Roy, A.H., Andreadis, K., He, X., and Butler, C., 2024, A multi-sensor approach to characterize winter water-level drawdown patterns in lakes: Remote Sensing, v. 16, no. 6, 947, 23 p., https://doi.org/10.3390/rs16060947.","productDescription":"947, 23 p.","ipdsId":"IP-159744","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":440171,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/rs16060947","text":"Publisher Index Page"},{"id":433622,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"16","issue":"6","noUsgsAuthors":false,"publicationDate":"2024-03-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Kumar, Abhishek","contributorId":342795,"corporation":false,"usgs":false,"family":"Kumar","given":"Abhishek","email":"","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":912753,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roy, Allison H. 0000-0002-8080-2729 aroy@usgs.gov","orcid":"https://orcid.org/0000-0002-8080-2729","contributorId":4240,"corporation":false,"usgs":true,"family":"Roy","given":"Allison","email":"aroy@usgs.gov","middleInitial":"H.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":910548,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Andreadis, Konstantinos","contributorId":258831,"corporation":false,"usgs":false,"family":"Andreadis","given":"Konstantinos","affiliations":[{"id":52307,"text":"Department of Civil and Environmental Engineering, University of Massachusetts Amherst, Amherst, Massachusetts, USA","active":true,"usgs":false}],"preferred":false,"id":912754,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"He, Xinchen","contributorId":316775,"corporation":false,"usgs":false,"family":"He","given":"Xinchen","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":912755,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Butler, Caitlyn","contributorId":316779,"corporation":false,"usgs":false,"family":"Butler","given":"Caitlyn","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":912756,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70252095,"text":"70252095 - 2024 - The influence of anthropogenic regulation and evaporite dissolution on earthquake-triggered ground failure","interactions":[],"lastModifiedDate":"2024-03-14T12:10:36.136417","indexId":"70252095","displayToPublicDate":"2024-03-08T07:03:39","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2842,"text":"Nature Communications","active":true,"publicationSubtype":{"id":10}},"title":"The influence of anthropogenic regulation and evaporite dissolution on earthquake-triggered ground failure","docAbstract":"Remote sensing observations of Searles Lake following the 2019 moment magnitude 7.1 Ridgecrest, California, earthquake reveal an area where surface ejecta is arranged in a repeating hexagonal pattern that is collocated with a solution-mining operation. By analyzing geologic and geotechnical data, here we show that the hexagonal surface ejecta is likely not a result of liquefaction. Instead, we propose dissolution cavity collapse (DCC) as an alternative driving mechanism. We support this theory with pre-event Interferometric Synthetic Aperture Radar data, which reveals differential subsidence patterns and the creation of subsurface void space. We also find that DCC is likely triggered at a lower shaking threshold than classical liquefaction. This and other unknown mechanisms can masquerade as liquefaction, introducing bias into liquefaction prediction models that rely on liquefaction inventories. This paper also highlights the opportunities and drawbacks of using remote sensing data to disentangle the complex factors that influence earthquake-triggered ground failure.
The amplitude and frequency content of background seismic noise is highly variable with geographic location. Understanding the characteristics and behavior of background seismic noise as a function of location can inform approaches to improve network performance and in turn increase earthquake detection capabilities. Here, we calculate power spectral density estimates in one‐hour windows for over 15 yr of vertical‐component data from the nine‐station Caribbean network (CU) and look at background noise within the 0.05–300 s period range. We describe the most visually apparent features observed at the CU stations. One of the most prominent features occurs in the 0.75–3 s band for which power levels are systematically elevated and decay as a function of proximity to the coastline. Further examination of this band on 1679 contiguous USArray Transportable Array stations reveals the same relationship. Such a relationship with coastal distance is not observed in the 4–8 s range more typical of globally observed secondary microseisms. A simple surface‐wave amplitude decay model fits the observed decay well with geometric spreading as the most important factor for stations near the coast (<∼50 km). The model indicates that power levels are strongly influenced by proximity to coastline at 0.75–3 s. This may be because power from nearshore wave action at 0.75–3 s overwhelms more distant and spatially distributed secondary microseism generation. Application of this basic model indicates that a power reduction of ∼25 dB can be achieved by simply installing the seismometer 25 km away from the coastline. This finding may help to inform future site locations and array design thereby improving network performance and data quality, and subsequently earthquake detection capabilities.
The deep sea is the largest biome on earth, but one of the least studied despite its critical role in global carbon cycling and climate buffering. Deep-sea organisms largely rely on particulate organic matter from the surface ocean for energy – these organisms in turn play critical roles in energy transport, transformation, storage, and sequestration of carbon. Within the deep sea, submarine canyons are amongst the most complex and dynamic environments in our oceans, where varied morphology, powerful currents, and variable nutrient conditions influence the distribution of species and transport of organic material throughout the water column and the seafloor. Significant habitat heterogeneity provides ideal substrates for cold-water corals, making submarine canyons of interest to conservation and management. However, how these and other topographic features in the deep ocean influence energy flow and trophic pathways is poorly known. Thus, submarine canyons serve as model systems to track variability in organic material flux and consequential utilization and assimilation by the benthos. In this study, we used an extensive stable isotope dataset to examine food-web structure in Baltimore and Norfolk submarine canyons and compared them to their adjacent slopes located along the U.S. Atlantic margin. Linear models were used to construct geospatially-explicit consumer isoscapes that predicted variation in carbon and nitrogen isotopes across the canyon-slope seascape, providing a predictive map from which to test hypotheses on the distribution and flow of energy resources, relevant to understanding whole community function. Communities were composed of isotopically diverse feeding groups with photosynthetically-derived organic carbon providing the basal food resource. Canyon communities were distinct from the slope, with canyon consumers significantly 13C-depleted, indicating a greater supply and/or utilization of fresh organic matter compared to the slope. Isoscapes for benthic and suspension feeders were distinct, possibly due to the consumption of different quality organic matter sources (fresh = suspension feeders, old = benthic feeders), each with distinct isotope composition. To our knowledge, our modeled isoscapes represent the first spatially extensive isotopic maps of deep-sea consumers, providing insights into regional-scale variation in stable carbon and nitrogen isotopes for different consumer groups. They provide a baseline for tracking climate-change induced fluctuations in the quality and availability of surface primary production and the consequential impact to benthic communities, which play critical roles in carbon cycling in our world’s oceans.
To better understand stream-fish sensitivity to fine sediment, we documented assemblage-wide responses by selected traits along a sedimentation gradient. We then discuss the management implications of these ‘dose–response’ relations in the contexts of biotic assessments and conservation of sediment-sensitive species. We identified a spatial gradient in sediment deposition among streams within the upper Piedmont of the Roanoke River basin in North Carolina and Virginia. We assessed fine-sediment sensitivity of 81 species based on eight species traits stratified by four attributes: food preference, feeding location, spawning substrate and spawning behaviour. We then ranked each trait and scored each species with respect to its sediment sensitivity. Using data from electrofishing surveys during 2018–2019, we calculated proportional abundances of traits observed at 30 sites throughout the study area and grouped species by their aggregate sensitivity scores. We assessed relations between embeddedness and silt cover and occurrences of species and traits using a combination of regression and ordination approaches. All traits tested responded to embeddedness or silt cover, or both. Feeding traits exhibited the strongest responses to embeddedness, while reproductive traits exhibited the strongest responses to silt cover. Our findings indicate that negative responses of the probability of presence for high-sensitivity traits to embeddedness and silt cover were linear, with no apparent thresholds. Additionally, proportional abundances of species with multiple high-sensitivity traits were inversely related to embeddedness and silt cover. Overall, our findings regarding population-level responses to sedimentation were consistent with our findings for trait-specific responses. Our analysis of species sensitivity to fine sediment corroborated the patterns we saw in our trait-specific analyses, indicating that population responses to sedimentation can be predicted from combinations of species traits. The ‘dose–response’ relations we documented may be applicable to managing sediment impacts on fishes, especially in the contexts of biotic assessments and conservation of sediment-sensitive species.
","language":"English","publisher":"Wiley","doi":"10.1111/eff.12768","usgsCitation":"Hirschler, M., Villamagna, A., Angermeier, P., and Laflamme, E., 2024, Deposited sediment influences occurrence of functional traits of stream fishes: Ecology of Freshwater Fish, v. 33, no. 3, e12768, 17 p., https://doi.org/10.1111/eff.12768.","productDescription":"e12768, 17 p.","ipdsId":"IP-145426","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":440188,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/eff.12768","text":"Publisher Index Page"},{"id":432043,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Carolina, Virginia","otherGeospatial":"Roanoke River drainage","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -79,\n 37\n ],\n [\n -81,\n 37\n ],\n [\n -81,\n 36\n ],\n [\n -79,\n 36\n ],\n [\n -79,\n 37\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"33","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-03-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Hirschler, Mallory","contributorId":340556,"corporation":false,"usgs":false,"family":"Hirschler","given":"Mallory","email":"","affiliations":[{"id":35056,"text":"Plymouth State University","active":true,"usgs":false}],"preferred":false,"id":907359,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Villamagna, Amy M.","contributorId":166683,"corporation":false,"usgs":false,"family":"Villamagna","given":"Amy M.","affiliations":[{"id":35056,"text":"Plymouth State University","active":true,"usgs":false}],"preferred":false,"id":907360,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Angermeier, Paul L. 0000-0003-2864-170X","orcid":"https://orcid.org/0000-0003-2864-170X","contributorId":204519,"corporation":false,"usgs":true,"family":"Angermeier","given":"Paul L.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":907361,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Laflamme, Eric","contributorId":340558,"corporation":false,"usgs":false,"family":"Laflamme","given":"Eric","email":"","affiliations":[{"id":25550,"text":"Virginia Polytechnic Institute and State University","active":true,"usgs":false}],"preferred":false,"id":907362,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70252054,"text":"70252054 - 2024 - Predicting redox conditions in groundwater at a national scale using random forest classification","interactions":[],"lastModifiedDate":"2024-03-26T15:02:02.331974","indexId":"70252054","displayToPublicDate":"2024-03-07T09:58:49","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":"Predicting redox conditions in groundwater at a national scale using random forest classification","docAbstract":"Redox conditions in groundwater may markedly affect the fate and transport of nutrients, volatile organic compounds, and trace metals, with significant implications for human health. While many local assessments of redox conditions have been made, the spatial variability of redox reaction rates makes the determination of redox conditions at regional or national scales problematic. In this study, redox conditions in groundwater were predicted for the contiguous United States using random forest classification by relating measured water quality data from over 30,000 wells to natural and anthropogenic factors. The model correctly predicted the oxic/suboxic classification for 78 and 79% of the samples in the out-of-bag and hold-out data sets, respectively. Variables describing geology, hydrology, soil properties, and hydrologic position were among the most important factors affecting the likelihood of oxic conditions in groundwater. Important model variables tended to relate to aquifer recharge, groundwater travel time, or prevalence of electron donors, which are key drivers of redox conditions in groundwater. Partial dependence plots suggested that the likelihood of oxic conditions in groundwater decreased sharply as streams were approached and gradually as the depth below the water table increased. The probability of oxic groundwater increased as base flow index values increased, likely due to the prevalence of well-drained soils and geologic materials in high base flow index areas. The likelihood of oxic conditions increased as topographic wetness index (TWI) values decreased. High topographic wetness index values occur in areas with a propensity for standing water and overland flow, conditions that limit the delivery of dissolved oxygen to groundwater by recharge; higher TWI values also tend to occur in discharge areas, which may contain groundwater with long travel times. A second model was developed to predict the probability of elevated manganese (Mn) concentrations in groundwater (i.e., ≥50 μg/L). The Mn model relied on many of the same variables as the oxic/suboxic model and may be used to identify areas where Mn-reducing conditions occur and where there is an increased risk to domestic water supplies due to high Mn concentrations. Model predictions of redox conditions in groundwater produced in this study may help identify regions of the country with elevated groundwater vulnerability and stream vulnerability to groundwater-derived contaminants.
","language":"English","publisher":"American Chemical Society","doi":"10.1021/acs.est.3c07576","usgsCitation":"Tesoriero, A.J., Wherry, S., Dupuy, D., and Johnson, T., 2024, Predicting redox conditions in groundwater at a national scale using random forest classification: Environmental Science and Technology, v. 58, no. 11, p. 5079-5092, https://doi.org/10.1021/acs.est.3c07576.","productDescription":"14 p.","startPage":"5079","endPage":"5092","ipdsId":"IP-154897","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":440191,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1021/acs.est.3c07576","text":"Publisher Index Page"},{"id":435024,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DVPJIX","text":"USGS data release","linkHelpText":"Input and results from a random forest 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]\n}","volume":"58","issue":"11","noUsgsAuthors":false,"publicationDate":"2024-03-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Tesoriero, Anthony J. 0000-0003-4674-7364 tesorier@usgs.gov","orcid":"https://orcid.org/0000-0003-4674-7364","contributorId":2693,"corporation":false,"usgs":true,"family":"Tesoriero","given":"Anthony","email":"tesorier@usgs.gov","middleInitial":"J.","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":896391,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wherry, Susan 0000-0002-6749-8697 swherry@usgs.gov","orcid":"https://orcid.org/0000-0002-6749-8697","contributorId":140159,"corporation":false,"usgs":true,"family":"Wherry","given":"Susan","email":"swherry@usgs.gov","affiliations":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"preferred":true,"id":896392,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dupuy, Danielle 0000-0001-9007-641X","orcid":"https://orcid.org/0000-0001-9007-641X","contributorId":222277,"corporation":false,"usgs":true,"family":"Dupuy","given":"Danielle","email":"","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":896393,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Johnson, Tyler D. 0000-0002-7334-9188","orcid":"https://orcid.org/0000-0002-7334-9188","contributorId":201888,"corporation":false,"usgs":true,"family":"Johnson","given":"Tyler D.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":896394,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70266297,"text":"70266297 - 2024 - Diurnal habitat selection and survival of elk neonates","interactions":[],"lastModifiedDate":"2025-05-05T14:39:33.672987","indexId":"70266297","displayToPublicDate":"2024-03-07T09:35:46","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3451,"text":"Southwestern Naturalist","active":true,"publicationSubtype":{"id":10}},"title":"Diurnal habitat selection and survival of elk neonates","docAbstract":"Natural selection should favor development of behaviors that increase survival, including juvenile survival. Habitat characteristics (e.g., hiding cover, forage quality and availability), maternal habitat selection, and microhabitat selection by the calf may influence elk (Cervus canadensis) calf survival and recruitment. We assessed diurnal microhabitat selection of bed sites by elk calves and calf-rearing areas selected by adult females to determine if these characteristics were associated with calf survival. We radio-tagged 33 elk calves in west-central New Mexico in 2011 and 55 calves in north-central New Mexico in 2012. We tracked calves daily to locate calf bedding sites and collected data on selected physical features and vegetation characteristics at used and paired random sites. The paired differences in these characteristics were then associated with calf fate. At the calf selection scale, for every 1-m difference in the distance to nearest concealment cover, the odds that a site was from a surviving calf increased by 7.8%. Habitat selection by adult females also was associated with calf survival. The odds of a bed site being from a surviving calf increased by 1.9% for every 1% difference in percentage of grass cover. High levels of concealment cover at the bed site were related to calf survival status. When we expanded selection to the adult level, females with surviving calves selected areas with higher grass cover, suggesting adult selection for higher forage availability while still providing high concealment cover for the calf.
","language":"English","publisher":"Southwestern Association of Naturalists","doi":"10.1894/0038-4909-67.3.205","usgsCitation":"Pitman, J.W., Cain, J.W., Gould, W., Tatman, N.M., and Liley, S.G., 2024, Diurnal habitat selection and survival of elk neonates: Southwestern Naturalist, v. 67, no. 3, p. 205-215, https://doi.org/10.1894/0038-4909-67.3.205.","productDescription":"11 p.","startPage":"205","endPage":"215","ipdsId":"IP-099298","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":485376,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"67","issue":"3","noUsgsAuthors":false,"publicationDate":"2024-03-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Pitman, James W.","contributorId":113799,"corporation":false,"usgs":false,"family":"Pitman","given":"James","email":"","middleInitial":"W.","affiliations":[{"id":24672,"text":"New Mexico Department of Game and Fish","active":true,"usgs":false}],"preferred":false,"id":935430,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cain, James W. III 0000-0003-4743-516X jwcain@usgs.gov","orcid":"https://orcid.org/0000-0003-4743-516X","contributorId":4063,"corporation":false,"usgs":true,"family":"Cain","given":"James","suffix":"III","email":"jwcain@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":935431,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gould, William R.","contributorId":244516,"corporation":false,"usgs":false,"family":"Gould","given":"William R.","affiliations":[{"id":27575,"text":"NMSU","active":true,"usgs":false}],"preferred":false,"id":935432,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tatman, Nicole M.","contributorId":338435,"corporation":false,"usgs":false,"family":"Tatman","given":"Nicole","email":"","middleInitial":"M.","affiliations":[{"id":24672,"text":"New Mexico Department of Game and Fish","active":true,"usgs":false}],"preferred":false,"id":935433,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Liley, Stewart G.","contributorId":337910,"corporation":false,"usgs":false,"family":"Liley","given":"Stewart","email":"","middleInitial":"G.","affiliations":[{"id":12628,"text":"New Mexico State University","active":true,"usgs":false}],"preferred":false,"id":935434,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70252213,"text":"70252213 - 2024 - Fine-resolution land cover mapping over large and mountainous areas for Lāna‘i, Hawaii using posterior probabilities, and expert knowledge","interactions":[],"lastModifiedDate":"2024-03-20T11:56:30.896168","indexId":"70252213","displayToPublicDate":"2024-03-07T06:54:06","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2068,"text":"International Journal of Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Fine-resolution land cover mapping over large and mountainous areas for Lāna‘i, Hawaii using posterior probabilities, and expert knowledge","docAbstract":"The task of accurately mapping species-specific vegetation cover in remote and topographically complex regions like those found in Hawaiʻi presents unique challenges. This study leverages a machine learning approach to accurately classify vegetation into fine species-specific classes across the island of Lāna‘i, Hawaii, offering a novel methodology for tackling such challenges. Utilizing high-resolution WordView-2 satellite imagery, a neural network classifier and a custom lidar-based geometric correction, we introduced two new approaches to refine our high-resolution land cover classifications. This included the implementation of prior-based adjustments to class posterior probabilities to enhance land cover classification accuracy. Moreover, we developed mixed hierarchical classification maps that use class posterior probabilities to identify, at the pixel level, the finest land cover class that meets a user-defined confidence threshold. The resulting high-resolution land cover map for Lāna‘i captures the rich diversity and distribution of native and invasive plant species with high overall accuracy, generally exceeding 95%, based on independent ground control data. The capacity to produce wall-to-wall species-level vegetation maps provides a new window into monitoring vegetation dynamics on Lāna‘i and similarly remote and topographically complex regions, and contributes to our broader understanding of ecosystem responses to invasive species, climatic changes, and land management practices such as erosion and sediment control planning. Our approach offers a blueprint for similar efforts in other complex and remote ecosystems.
Improving the interpretability of phase‐picking neural networks remains an important task to facilitate their deployment to routine, real‐time seismic monitoring. The popular phase‐picking neural networks published in the literature lack interpretability because their output prediction scores do not necessarily correspond with the reliability of phase picks and can even be highly inconsistent depending on how we window the waveform data. Here, we show that systematically shifting the waveforms during training and using an antialiasing filter within the neural network architecture can substantially improve the consistency of the output prediction scores and can even make them scale with the signal‐to‐noise ratios of the waveforms. We demonstrate the improvements by applying these approaches to a commonly used phase‐picking neural network architecture and using waveform data from the 2019 Ridgecrest earthquake sequence.
","language":"English","publisher":"Seismological Society of America","doi":"10.1785/0320230054","usgsCitation":"Park, Y., Delbridge, B.G., and Shelly, D.R., 2024, Making phase-picking neural networks more consistent and interpretable: The Seismic Record, v. 4, no. 1, p. 72-80, https://doi.org/10.1785/0320230054.","productDescription":"9 p.","startPage":"72","endPage":"80","ipdsId":"IP-161641","costCenters":[{"id":78686,"text":"Geologic Hazards Science Center - Seismology / Geomagnetism","active":true,"usgs":true}],"links":[{"id":487634,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1785/0320230054","text":"Publisher Index Page"},{"id":481875,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"4","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-03-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Park, Yongsoo","contributorId":350716,"corporation":false,"usgs":false,"family":"Park","given":"Yongsoo","affiliations":[{"id":48588,"text":"Los Alamos National Lab","active":true,"usgs":false}],"preferred":false,"id":926903,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Delbridge, Brent G. 0000-0003-2808-8772","orcid":"https://orcid.org/0000-0003-2808-8772","contributorId":192986,"corporation":false,"usgs":false,"family":"Delbridge","given":"Brent","email":"","middleInitial":"G.","affiliations":[],"preferred":false,"id":926904,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shelly, David R. 0000-0003-2783-5158 dshelly@usgs.gov","orcid":"https://orcid.org/0000-0003-2783-5158","contributorId":206750,"corporation":false,"usgs":true,"family":"Shelly","given":"David","email":"dshelly@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":926905,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70257488,"text":"70257488 - 2024 - Modeling protective action decision-making in earthquakes by using explainable machine learning and video data","interactions":[],"lastModifiedDate":"2024-08-16T16:17:09.924669","indexId":"70257488","displayToPublicDate":"2024-03-05T10:56:10","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":"Modeling protective action decision-making in earthquakes by using explainable machine learning and video data","docAbstract":"Earthquakes pose substantial threats to communities worldwide. Understanding how people respond to the fast-changing environment during earthquakes is crucial for reducing risks and saving lives. This study aims to study people’s protective action decision-making in earthquakes by leveraging explainable machine learning and video data. Specifically, this study first collected real-world CCTV footage and video postings from social media platforms, and then identified and annotated changes in the environment and people’s behavioral responses during the M7.1 2018 Anchorage earthquake. By using the fully annotated video data, we applied XGBoost, a widely-used machine learning method, to model and forecast people’s protective actions (e.g., drop and cover, hold on, and evacuate) during the earthquake. Then, explainable machine learning techniques were used to reveal the complex, nonlinear relationships between different factors and people’s choices of protective actions. Modeling results confirm that social and environmental cues played critical roles in affecting the probability of different protective actions. Certain factors, such as the earthquake shaking intensity and number of people shown in the environment, displayed evident nonlinear relationships with the probability of choosing to evacuate. These findings can help emergency managers and policymakers design more effective protective action recommendations during earthquakes.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-024-55584-7","usgsCitation":"Zhang, X., Zhao, X., Baldwin, D., McBride, S., Bellizzi, J., Cochran, E.S., Luco, N., Wood, M., and Cova, T.J., 2024, Modeling protective action decision-making in earthquakes by using explainable machine learning and video data: Scientific Reports, v. 14, 5480, 13 p., https://doi.org/10.1038/s41598-024-55584-7.","productDescription":"5480, 13 p.","ipdsId":"IP-162087","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true},{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":440208,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-024-55584-7","text":"Publisher Index Page"},{"id":432865,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"14","noUsgsAuthors":false,"publicationDate":"2024-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Zhang, Xiaojian","contributorId":214967,"corporation":false,"usgs":false,"family":"Zhang","given":"Xiaojian","email":"","affiliations":[{"id":39141,"text":"Department of Basic Science, College of Veterinary Medicine, Mississippi State University, 9 Mississippi, United States;","active":true,"usgs":false}],"preferred":false,"id":910521,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Zhao, Xilei","contributorId":342942,"corporation":false,"usgs":false,"family":"Zhao","given":"Xilei","email":"","affiliations":[{"id":36221,"text":"University of Florida","active":true,"usgs":false}],"preferred":false,"id":910522,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Baldwin, Dare","contributorId":269660,"corporation":false,"usgs":false,"family":"Baldwin","given":"Dare","email":"","affiliations":[],"preferred":false,"id":910523,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McBride, Sara K. 0000-0002-8062-6542","orcid":"https://orcid.org/0000-0002-8062-6542","contributorId":206933,"corporation":false,"usgs":true,"family":"McBride","given":"Sara K.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":910524,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bellizzi, Josephine","contributorId":342943,"corporation":false,"usgs":false,"family":"Bellizzi","given":"Josephine","email":"","affiliations":[{"id":6604,"text":"University of Oregon","active":true,"usgs":false}],"preferred":false,"id":910525,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cochran, Elizabeth S. 0000-0003-2485-4484 ecochran@usgs.gov","orcid":"https://orcid.org/0000-0003-2485-4484","contributorId":2025,"corporation":false,"usgs":true,"family":"Cochran","given":"Elizabeth","email":"ecochran@usgs.gov","middleInitial":"S.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":910526,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Luco, Nicolas 0000-0002-5763-9847 nluco@usgs.gov","orcid":"https://orcid.org/0000-0002-5763-9847","contributorId":140191,"corporation":false,"usgs":true,"family":"Luco","given":"Nicolas","email":"nluco@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":false,"id":910527,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wood, Matthew","contributorId":342944,"corporation":false,"usgs":false,"family":"Wood","given":"Matthew","email":"","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":910528,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Cova, Thomas J.","contributorId":342946,"corporation":false,"usgs":false,"family":"Cova","given":"Thomas","email":"","middleInitial":"J.","affiliations":[{"id":13252,"text":"University of Utah","active":true,"usgs":false}],"preferred":false,"id":910529,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70252474,"text":"70252474 - 2024 - Small forested watershed weathers effects of climate change better than a nearby urban watershed in Northern Virginia, USA","interactions":[],"lastModifiedDate":"2024-03-26T12:14:35.348075","indexId":"70252474","displayToPublicDate":"2024-03-05T07:11:49","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1924,"text":"Hydrological Processes","active":true,"publicationSubtype":{"id":10}},"title":"Small forested watershed weathers effects of climate change better than a nearby urban watershed in Northern Virginia, USA","docAbstract":"South Fork Quantico Creek (SFQ; 19.8 square kilometre (km2), forested) and Fourmile Run (4MR; 32.4-km2, urban) are small watersheds in northern Virginia, United States. Precipitation and streamflow data for both watersheds were examined from water year (WY) 1952 through 2022. Temporal changes in hydrologic metrics were identified by calculating trends in annual precipitation, annual peak flow, mean daily flow, minimum daily flow, stream flashiness, and the runoff ratio. The impact of climate and urbanization on watershed hydrology was assessed by computing trends on both raw and precipitation-adjusted data. Despite increasing precipitation in both watersheds, increasing monotonic trends in most hydrologic metrics were observed only in 4MR. At 4MR, the long-term trend in annual peak flow was non-linear, thus trends were calculated on separate periods. Annual peak flow increased from WY 1952 through 1968, coinciding with a period of rapid urbanization. During WY 1969 through 1981, annual peak flows decreased, coinciding with construction of a flood channelization project. Trends for both periods were robust to precipitation adjustment. From WY 1982 through 2022, no change in the precipitation-adjusted annual peak flows occurred, suggesting annual peak flows increased due to climate factors during this period. Comparison of area-normalized hydrologic metrics between the two watersheds revealed higher flows in 4MR than SFQ across all flows, not just high flows. Runoff ratio and stream flashiness also were higher in 4MR. Differences in hydrologic metrics between the two watersheds were driven primarily by differences in land use, land cover, and modifications to the water balance related to urbanization. Climate change has altered watershed hydrology at both sites, but extensive urbanization in 4MR has altered the hydrology more than that of SFQ. We conclude that urban watersheds are likely at greater risk of increased flooding than less developed areas as the climate intensifies.
The global decline of freshwater mussels and their crucial ecological services highlight the need to understand their phylogeny, phylogeography and patterns of genetic diversity to guide conservation efforts. Such knowledge is urgently needed for Unio crassus, a highly imperilled species originally widespread throughout Europe and southwest Asia. Recent studies have resurrected several species from synonymy based on mitochondrial data, revealing U. crassus to be a complex of cryptic species. To address long-standing taxonomic uncertainties hindering effective conservation, we integrate morphometric, phylogenetic, and phylogeographic analyses to examine species diversity within the U. crassus complex across its entire range. Phylogenetic analyses were performed using cytochrome c oxidase subunit I (815 specimens from 182 populations) and, for selected specimens, whole mitogenome sequences and Anchored Hybrid Enrichment (AHE) data on ∼600 nuclear loci. Mito-nuclear discordance was detected, consistent with mitochondrial DNA gene flow between some species during the Pliocene and Pleistocene. Fossil-calibrated phylogenies based on AHE data support a Mediterranean origin for the U. crassus complex in the Early Miocene. The results of our integrative approach support 12 species in the group: the previously recognised Unio bruguierianus, Unio carneus, Unio crassus, Unio damascensis, Unio ionicus, Unio sesirmensis, and Unio tumidiformis, and the reinstatement of five nominal taxa: Unio desectus stat. rev., Unio gontierii stat. rev., Unio mardinensis stat. rev., Unio nanus stat. rev., and Unio vicarius stat. rev. Morphometric analyses of shell contours reveal important morphospace overlaps among these species, highlighting cryptic, but geographically structured, diversity. The distribution, taxonomy, phylogeography, and conservation of each species are succinctly described.
Interactions between wildlife and livestock can lead to cross-species disease transmission, which incurs economic costs and threatens wildlife conservation. Wild waterfowl are natural hosts of avian influenza viruses (AIVs), are often abundant near poultry farms, and have been linked to outbreaks of AIVs in poultry. Interspecific and seasonal variation in waterfowl movement and habitat use means that the risk of disease transmission between wild birds and poultry inevitably varies across species, space, and time. Here, we used GPS telemetry data from 10 waterfowl species in and near California's Central Valley, a region where both wild waterfowl and domestic poultry are abundant, to study selection of poultry farms by waterfowl across diel, seasonal, and annual cycles. We found that waterfowl selected for wetlands, open water, protected areas, and croplands, which meant that they generally avoided habitats that were likely to be used for poultry farming. These selection patterns were linked to species' ecology and diel behavioral patterns, such that avoidance of poultry habitats was stronger for local or partial migrants than for long-distance migrants, and stronger during daytime than at night. We then combined these habitat selection results with data on poultry farm locations to map risk of waterfowl–poultry contact across the Central Valley. Average selection strength at poultry farms was low, suggesting that current placement of poultry farms is generally effective for limiting risk of contact with wild birds. When we combined these habitat selection results with data on species' abundances and AIV infection prevalence, we found dramatic variation in potential AIV transmission risk among species. These results could be used to prioritize surveillance and biosecurity efforts for regions and times of relatively high risk. More generally, these results highlight that fine-scale movement data can help identify interspecific, seasonal, and diel patterns in animal behaviors that affect wildlife and poultry health.
Preferential groundwater discharge features along stream corridors are ecologically important at local and stream network scales, yet we lack quantification of the multiscale controls on the spatial patterning of groundwater discharge. Here we identify physical attributes that best explain variation in the presence and lateral extent of preferential groundwater discharges along two 5th order streams, the Housatonic and Farmington Rivers, and 32 1st to 4th order reaches across the Farmington River network. We mapped locations of preferential groundwater discharge exposed along streambanks using handheld thermal infrared cameras paired with high-resolution topographic and land use land cover datasets, surficial soil characteristic maps, and depth-to-bedrock geophysical measurements. The unconfined Housatonic River, MA, USA (12 km) had fewer discharge locations and less lateral extent (41 discharge locations with 38 m of active discharge/km of river) compared to the partially confined Farmington River, CT, USA (26 km; 169 discharge locations with 129 m of active discharge/km of river). Using a moving window analysis, we found along both rivers that discharge was more likely to occur where bank slopes were steeper, floodplain extent was narrower, and degree of confinement was higher. Along the Farmington River, groundwater discharge was more likely to occur where saturated hydraulic conductivity was higher and depth-to-bedrock was shallower. Among the 32 stream reaches surveyed (33.2 km of total stream length) within the Farmington River watershed, preferential discharge was observed in all but two stream reaches, varied from 0 to 25% of lateral extent along stream banks (mean = 6%), and was more likely to occur where stream reach slopes were steep, saturated hydraulic conductivity was high, and watershed urbanization was low. Our results show that, though both surface (e.g., topographic, land use land cover) and subsurface (e.g., soil characteristics, bedrock depth) factors control the prevalence of streambank preferential groundwater discharge, the dominant controls vary across valley settings and stream sizes.
The ability to sex individuals is an important component of many behavioural and ecological investigations and provides information for demographic models used in conservation and species management. However, many birds are difficult to sex using morphological characters or traditional molecular sexing methods. In this study, we developed probabilistic models for sexing birds using quantitative PCR (qPCR) data. First, we quantified distributions of gene copy numbers at a set of six sex-linked genes, including the sex-determining gene DMRT1, for individuals across 17 species and seven orders of birds (n = 150). Using these data, we built predictive logistic models for sex identification and tested their performance with independent samples from 51 species and 13 orders (n = 209). Models using the two loci most highly correlated with sex had greater accuracy than models using the full set of sex-linked loci, across all taxonomic levels of analysis. Sex identification was highly accurate when individuals to be assigned were of species used in model building. Our analytical approach was widely applicable across diverse neognath bird lineages spanning millions of years of evolutionary divergence. Unlike previous methods, our probabilistic framework incorporates uncertainty around qPCR measurements as well as biological variation within species into decision-making rules. We anticipate that this method will be useful for sexing birds, including those of high conservation concern and/or subsistence value, that have proven difficult to sex using traditional approaches. Additionally, the general analytical framework presented in this paper may also be applicable to other organisms with sex chromosomes.
Aufeis (also known as icings) are large sheet-like masses of layered ice that form in river channels in arctic environments in the winter as groundwater discharges to the land surface and subsequently freezes. Aufeis are important sources of water for Arctic river ecosystems, bolstering late summer river discharge and providing habitat for caribou escaping insect harassment. The aim of this research is to use numerical simulations to evaluate a conceptual model of subsurface hydrogeothermal conditions that can lead to the formation of aufeis. We used a conceptual model based on geophysical data from the Kuparuk aufeis field on the North Slope of Alaska to develop a two-dimensional heterogeneous vertical profile model of groundwater flow, heat transport, and freeze/thaw dynamics. Modelling results showed that groundwater can flow to the land surface through subvertical high permeability pathways during winter months when the lower permeability soils near the land surface are frozen. The groundwater discharge can freeze on the surface, contributing to aufeis formation throughout the winter. We performed sensitivity analyses on subsurface properties and surface temperature and found that aufeis formation is most sensitive to the volume of unfrozen water available in the subsurface and the rate at which the subsurface water travels to the land surface. Although a trend of warming air temperatures will lead to a greater volume of unfrozen subsurface water, the aufeis volume can be reduced under warming conditions if the period of time for which air temperatures are below freezing is reduced.
Among polar bears (Ursus maritimus), only parturient females den for extended periods, emerging from maternal dens in spring after having substantially depleted their energy reserves during a fast that can exceed 8 months. Although den emergence coincides with a period of increasing prey availability, polar bears typically do not depart immediately to hunt, but instead remain at the den for up to a month. This delay suggests that there are likely adaptive advantages to remaining at the den between emergence and departure, but the influence of the timing and duration of this post-emergence period on cub survival has not been evaluated previously. We used temperature and location data from 70 denning bears collared within the Southern Beaufort Sea and Chukchi Sea subpopulations to estimate the phenology of the post-emergence period. We evaluated the influence of various spatial and temporal features on duration of the post-emergence period and evaluated the potential influence of post-emergence duration on litter survival early in the spring following denning. For dens that likely contained viable cubs at emergence (n = 56), mean den emergence occurred on 16 March (SE = 1.4 days) and mean departure on 24 March (SE = 1.6 days), with dates typically occurring later in the Chukchi Sea relative to Southern Beaufort Sea and on land relative to sea ice. Mean duration of the post-emergence period was 7.9 days (SE = 1.4) for bears that were observed with cubs later in the spring, which was over 4 times longer than duration of those observed without cubs (1.9 days). Litter survival in the spring following denning (n = 31 dens) increased from 0.5 to 0.9 when duration of the post-emergence period increased by ~4 days and other variables were held at mean values. Our limited sample size and inability to verify cub presence at emergence suggests that future research is merited to improve our understanding of this relationship. Nonetheless, our results highlight the importance of the post-emergence period in contributing to reproductive success and can assist managers in developing conservation and mitigation strategies in denning areas, which will be increasingly important as human activities expand in the Arctic.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/jmammal/gyae010","usgsCitation":"Andersen, E., Wilson, R., Rode, K.D., Durner, G.M., Atwood, T.C., and Gustine, D., 2024, The post-emergence period for denning polar bears: Phenology and influence on cub survival: Journal of Mammalogy, gyae010, 12 p., https://doi.org/10.1093/jmammal/gyae010.","productDescription":"gyae010, 12 p.","ipdsId":"IP-149193","costCenters":[{"id":65299,"text":"Alaska Science Center Ecosystems","active":true,"usgs":true}],"links":[{"id":440246,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jmammal/gyae010","text":"Publisher Index Page"},{"id":435028,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7G73BTD","text":"USGS data release","linkHelpText":"Estimated Post-Emergence Period for Denning Polar Bears of the Chukchi and Beaufort Seas"},{"id":426357,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2024-03-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Andersen, Erik","contributorId":334600,"corporation":false,"usgs":false,"family":"Andersen","given":"Erik","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":896013,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Wilson, Ryan R. ","contributorId":222456,"corporation":false,"usgs":false,"family":"Wilson","given":"Ryan R. ","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":896014,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rode, Karyn D. 0000-0002-3328-8202 krode@usgs.gov","orcid":"https://orcid.org/0000-0002-3328-8202","contributorId":5053,"corporation":false,"usgs":true,"family":"Rode","given":"Karyn","email":"krode@usgs.gov","middleInitial":"D.","affiliations":[{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":896015,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Durner, George M. 0000-0002-3370-1191 gdurner@usgs.gov","orcid":"https://orcid.org/0000-0002-3370-1191","contributorId":3576,"corporation":false,"usgs":true,"family":"Durner","given":"George","email":"gdurner@usgs.gov","middleInitial":"M.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":896016,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Atwood, Todd C. 0000-0002-1971-3110 tatwood@usgs.gov","orcid":"https://orcid.org/0000-0002-1971-3110","contributorId":4368,"corporation":false,"usgs":true,"family":"Atwood","given":"Todd","email":"tatwood@usgs.gov","middleInitial":"C.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":896017,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gustine, David","contributorId":200449,"corporation":false,"usgs":false,"family":"Gustine","given":"David","affiliations":[],"preferred":false,"id":896018,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70251873,"text":"70251873 - 2024 - Characterization of change in tree cover state and condition over the conterminous United States","interactions":[],"lastModifiedDate":"2024-03-05T12:39:16.803118","indexId":"70251873","displayToPublicDate":"2024-03-02T06:37:27","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1689,"text":"Forests","active":true,"publicationSubtype":{"id":10}},"title":"Characterization of change in tree cover state and condition over the conterminous United States","docAbstract":"As part of a multi-year project, funded by the California Energy Commission, annual magnetotelluric (MT) surveys have been collected at The Geysers geothermal field in northern California with the goal of measuring temporal changes within the steam field. The repeat surveys started in 2021 and repeated a 2017 survey (Peacock et al., 2020) with further extension to the southern part of the geothermal field. Temporal variations in the MT transfer functions are observed to be spatially coherent and compartmentalized. Mapping residual phase tensor ellipses demonstrates the direction of maximum change is often aligned with existing fracture orientations. Three dimensional inversion of the MT data, using the inversion results from previous years as the starting model, indicates that the steam reservoir has generally become more resistive over time (~10%), suggesting more steam in the field. A few pockets within the steam field have become more electrically conductive over time and are collocated with injection wells, suggesting either more fluid content in those zones, less steam, or more saline fluids.
","conferenceTitle":"49th Workshop on Geothermal Reservoir Engineering","conferenceDate":"February 12-14, 2024","conferenceLocation":"Stanford, CA","language":"English","publisher":"Stanford University","usgsCitation":"Peacock, J.R., Alumbaugh, D., Mitchell, M.A., and Hartline, C., 2024, Summary of annual repeat magnetotelluric surveys of the Geysers geothermal field, 49th Workshop on Geothermal Reservoir Engineering, Stanford, CA, February 12-14, 2024, 6 p.","productDescription":"6 p.","ipdsId":"IP-161861","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":501541,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":501540,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://pangea.stanford.edu/ERE/db/IGAstandard/record_detail.php?id=36417","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California","otherGeospatial":"Geysers geothermal field","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -122.9167,\n 38.9\n ],\n [\n -122.9167,\n 38.7333\n ],\n [\n -122.667,\n 38.7333\n ],\n [\n -122.667,\n 38.9\n ],\n [\n -122.9167,\n 38.9\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Peacock, Jared R. 0000-0002-0439-0224 jpeacock@usgs.gov","orcid":"https://orcid.org/0000-0002-0439-0224","contributorId":4996,"corporation":false,"usgs":true,"family":"Peacock","given":"Jared","email":"jpeacock@usgs.gov","middleInitial":"R.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":true,"id":899089,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Alumbaugh, David 0000-0002-6975-7197","orcid":"https://orcid.org/0000-0002-6975-7197","contributorId":299109,"corporation":false,"usgs":false,"family":"Alumbaugh","given":"David","email":"","affiliations":[{"id":64775,"text":"Berkeley National Lab","active":true,"usgs":false}],"preferred":false,"id":899090,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mitchell, Michael Albert 0000-0001-5070-8793","orcid":"https://orcid.org/0000-0001-5070-8793","contributorId":299110,"corporation":false,"usgs":true,"family":"Mitchell","given":"Michael","email":"","middleInitial":"Albert","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":899091,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hartline, C.","contributorId":335661,"corporation":false,"usgs":false,"family":"Hartline","given":"C.","affiliations":[{"id":38755,"text":"Calpine","active":true,"usgs":false}],"preferred":false,"id":899092,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70261865,"text":"70261865 - 2024 - Delta blue(green)s: The effect of drought and drought-management actions on microcystis in the Sacramento–San Joaquin Delta","interactions":[],"lastModifiedDate":"2024-12-31T17:01:30.344758","indexId":"70261865","displayToPublicDate":"2024-03-01T10:52:54","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":"Delta blue(green)s: The effect of drought and drought-management actions on microcystis in the Sacramento–San Joaquin Delta","docAbstract":"Cyanobacterial phytoplankton blooms are more prevalent in the freshwater Sacramento-San Joaquin Delta (Delta) since the late 1990s, including blooms driven by overgrowths of potentially toxigenic organisms of the genus Microcystis. Data from 2014 to 2021 were used to show how flow dynamics, water temperature, and water clarity drive occurrence of Microcystis. We used a Microcystis bloom in the central Delta from 2021 as a case study for how novel monitoring tools can track blooms in real-time and be used post hoc to evaluate the effects of management actions.
Microcystis was detected throughout the Delta in all but the highest-flow years, and bloom incidence and severity increased during drier years. In the South Delta, Franks Tract, lower San Joaquin River, and Old River regions, where blooms are most prevalent, higher water temperatures and clarities combined with lower exports from state and federal water projects were the best explanatory factors for the occurrence of Microcystis blooms. Nutrient concentrations were lower in summer than in winter, but only became limiting at high phytoplankton concentrations.
We used satellite data and in situ continuous monitoring of flow, phytoplankton communities, and water quality to track hydro-biogeochemical conditions during the 2021 case study Microcystis bloom in the Central Delta. We did not find evidence that changes to Delta outflow regulatory standards contributed to this bloom, but changes in flow caused by a salinity barrier placed in west False River may have exacerbated the bloom. The frequency and severity of droughts are expected to increase in the future as a result of climate change, and our study demonstrates how continued monitoring of cyanotoxins, water quality, and phytoplankton communities could help improve management of cyanobacterial blooms in the Delta and other estuaries.
","language":"English","publisher":"University of California Davis","doi":"10.15447/sfews.2024v22iss1art2","usgsCitation":"Bouma-Gregson, K., Bosworth, D., Flynn, T., Maguire, A., Rinde, J., and Hartman, R., 2024, Delta blue(green)s: The effect of drought and drought-management actions on microcystis in the Sacramento–San Joaquin Delta: San Francisco Estuary and Watershed Science, v. 22, no. 1, 2, 39 p., https://doi.org/10.15447/sfews.2024v22iss1art2.","productDescription":"2, 39 p.","ipdsId":"IP-149616","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":467026,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.15447/sfews.2024v22iss1art2","text":"Publisher Index Page"},{"id":465580,"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 -121.66242740252514,\n 38.73105407088619\n ],\n [\n -122.63741310268495,\n 38.73105407088619\n ],\n [\n -122.63741310268495,\n 37.06608164483798\n ],\n [\n -121.66242740252514,\n 37.06608164483798\n ],\n [\n -121.66242740252514,\n 38.73105407088619\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"22","issue":"1","noUsgsAuthors":false,"publicationDate":"2024-03-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Bouma-Gregson, Keith 0000-0002-0304-6034","orcid":"https://orcid.org/0000-0002-0304-6034","contributorId":311235,"corporation":false,"usgs":true,"family":"Bouma-Gregson","given":"Keith","email":"","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":922083,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bosworth, David 0000-0003-0740-3390","orcid":"https://orcid.org/0000-0003-0740-3390","contributorId":347649,"corporation":false,"usgs":false,"family":"Bosworth","given":"David","affiliations":[{"id":40593,"text":"CA Department of Water Resources","active":true,"usgs":false}],"preferred":false,"id":922084,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Flynn, Theodore M. 0000-0002-1838-8942","orcid":"https://orcid.org/0000-0002-1838-8942","contributorId":347651,"corporation":false,"usgs":false,"family":"Flynn","given":"Theodore M.","affiliations":[{"id":40593,"text":"CA Department of Water Resources","active":true,"usgs":false}],"preferred":false,"id":922085,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Maguire, Amanda","contributorId":347652,"corporation":false,"usgs":false,"family":"Maguire","given":"Amanda","affiliations":[{"id":40593,"text":"CA Department of Water Resources","active":true,"usgs":false}],"preferred":false,"id":922086,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rinde, Jenna 0000-0001-6677-4701","orcid":"https://orcid.org/0000-0001-6677-4701","contributorId":347655,"corporation":false,"usgs":false,"family":"Rinde","given":"Jenna","affiliations":[{"id":6952,"text":"California Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":922087,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hartman, Rosemary","contributorId":200388,"corporation":false,"usgs":false,"family":"Hartman","given":"Rosemary","email":"","affiliations":[],"preferred":false,"id":922088,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70257392,"text":"70257392 - 2024 - Second guessing the maximum likelihood estimator values for bat surveys","interactions":[],"lastModifiedDate":"2026-02-10T18:07:14.5025","indexId":"70257392","displayToPublicDate":"2024-03-01T10:37:14","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3909,"text":"Journal of the Southeastern Association of Fish and Wildlife Agencies","active":true,"publicationSubtype":{"id":10}},"title":"Second guessing the maximum likelihood estimator values for bat surveys","docAbstract":"TThe U.S. Fish and Wildlife Service allows acoustical surveys and automated identification software to determine the presence of the endangered northern long-eared bat (Myotis septentrionalis) and Indiana bat (Myotis sodalis). Analytical software is required to assess presence probability on a site-night basis using a maximum likelihood estimator (MLE) that accounts for interspecific bat misclassification rates. The current standard for occupancy is a returned MLE P-value < 0.05 at the nightly level irrespective of the number of files identified as either northern long-eared bats or Indiana bats. These MLE P-values can vary based on presence of other bat species with similar calls and the relative proportions of all species recorded. Accordingly, there is concern that with few nightly northern long-eared bat or Indiana bat recordings or the presence of large numbers of high-frequency bats, false-negative findings from a swamping effect could result. Using data collected in 2020–2021 by the U.S. Fish and Wildlife Service to set nationwide acoustic monitoring guidelines, we examined the relationship of returned software MLE P-values from 4873 site-nights of acoustic detector data relative to nightly counts of northern long-eared bats and Indiana bats, overall counts of other high-frequency bats, and habitat cover type. For both northern long-eared bats and Indiana bats, nights with one or more echolocation pass files identified as either species but above the MLE P-value threshold largely occurred where nightly counts of the target species was <15 and their proportion to the count of high-frequency bat species was low. We followed this analysis with a simulation using a known call library and observed similar patterns. Accordingly, with few nightly cholocation passes, post-hoc visual assessment following automated software identification easily could be undertaken. Evidence of swamping by other high-frequency species causing positive file identification creating false-negative or false-positives of northern long-eared bats and Indiana bats was not apparent at nightly counts of either species > 10.
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Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":910218,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"De La Cruz, Jesse L.","contributorId":342611,"corporation":false,"usgs":false,"family":"De La Cruz","given":"Jesse L.","affiliations":[{"id":81893,"text":"Virginia Polytechnic and State University","active":true,"usgs":false}],"preferred":false,"id":910223,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thorne, Emily D.","contributorId":342606,"corporation":false,"usgs":false,"family":"Thorne","given":"Emily D.","affiliations":[{"id":36189,"text":"National Park Service","active":true,"usgs":false}],"preferred":false,"id":910219,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Silvis, Alexander","contributorId":342607,"corporation":false,"usgs":false,"family":"Silvis","given":"Alexander","affiliations":[{"id":40299,"text":"West Virginia Division of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":910220,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Armstrong, Michael P.","contributorId":342608,"corporation":false,"usgs":false,"family":"Armstrong","given":"Michael P.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":910221,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"King, R. Andrew","contributorId":342609,"corporation":false,"usgs":false,"family":"King","given":"R.","email":"","middleInitial":"Andrew","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":910222,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70263334,"text":"70263334 - 2024 - Second guessing the maximum likelihood estimator values for bat surveys.","interactions":[],"lastModifiedDate":"2025-02-06T16:31:28.382195","indexId":"70263334","displayToPublicDate":"2024-03-01T10:27:24","publicationYear":"2024","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3909,"text":"Journal of the Southeastern Association of Fish and Wildlife Agencies","active":true,"publicationSubtype":{"id":10}},"title":"Second guessing the maximum likelihood estimator values for bat surveys.","docAbstract":"The U.S. Fish and Wildlife Service allows acoustical surveys and automated identification software to determine the presence of the endangered northern long-eared bat (Myotis septentrionalis) and Indiana bat (Myotis sodalis). Analytical software is required to assess presence probability on a site-night basis using a maximum likelihood estimator (MLE) that accounts for interspecific bat misclassification rates. The current standard for occupancy is a returned MLE P-value < 0.05 at the nightly level irrespective of the number of files identified as either northern long-eared bats or Indiana bats. These MLE P-values can vary based on presence of other bat species with similar calls and the relative proportions of all species recorded. Accordingly, there is concern that with few nightly northern long-eared bat or Indiana bat recordings or the presence of large numbers of high-frequency bats, false-negative findings from a swamping effect could result. Using data collected in 2020–2021 by the U.S. Fish and Wildlife Service to set nationwide acoustic monitoring guidelines, we examined the relationship of returned software MLE P-values from 4873 site-nights of acoustic detector data relative to nightly counts of northern long-eared bats and Indiana bats, overall counts of other high-frequency bats, and habitat cover type. For both northern long-eared bats and Indiana bats, nights with one or more echolocation pass files identified as either species but above the MLE P-value threshold largely occurred where nightly counts of the target species was <15 and their proportion to the count of high-frequency bat species was low. We followed this analysis with a simulation using a known call library and observed similar patterns. Accordingly, with few nightly cholocation passes, post-hoc visual assessment following automated software identification easily could be undertaken. Evidence of swamping by other high-frequency species causing positive file identification creating false-negative or false-positives of northern long-eared bats and Indiana bats was not apparent at nightly counts of either species > 10.
","language":"English","publisher":"Southeastern Association of Fish and Wildlife Agencies","usgsCitation":"Ford, W., De La Cruz, J.L., Thorne, E., Silvis, A., Armstrong, M., and King, R.A., 2024, Second guessing the maximum likelihood estimator values for bat surveys.: Journal of the Southeastern Association of Fish and Wildlife Agencies, v. 11, p. 177-184.","productDescription":"8 p.","startPage":"177","endPage":"184","ipdsId":"IP-167031","costCenters":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"links":[{"id":481729,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://seafwa.org/journal/2024/second-guessing-maximum-likelihood-estimator-values-bat-surveys"},{"id":481754,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Arkansas, Illinois, Indiana, Kentucky, Missouri, New Jersey, Ohio, Tennessee, Virginia, West Virginia, Wisconsin","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"MultiPolygon\",\"coordinates\":[[[[-85.605165,34.984678],[-85.188741,32.889727],[-84.925427,32.221551],[-85.141831,31.839261],[-84.999626,31.009079],[-87.598928,30.997457],[-87.615367,30.837031],[-87.39643,30.617734],[-87.440678,30.391498],[-87.656888,30.249709],[-88.014572,30.222366],[-87.766626,30.262353],[-88.008396,30.684956],[-88.191542,30.317002],[-88.402283,30.510852],[-88.468879,31.930262],[-88.097888,34.892202],[-88.172102,34.955213],[-90.309297,34.995694],[-90.301957,34.880053],[-90.453916,34.891122],[-90.469897,34.72703],[-90.565437,34.736536],[-90.486966,34.701477],[-90.657488,34.322231],[-90.89456,34.22438],[-90.870461,34.082739],[-91.048367,33.985078],[-91.000107,33.799549],[-91.125527,33.70878],[-91.046778,33.706313],[-91.205377,33.700819],[-91.191973,33.417728],[-91.064701,33.453775],[-91.141615,33.299539],[-91.05873,33.286901],[-91.183662,33.141691],[-91.124639,33.064127],[-91.265018,33.005084],[-94.024475,33.019207],[-94.043375,33.542315],[-94.485577,33.65331],[-94.432015,35.367391],[-94.617814,36.577732],[-94.605734,39.122204],[-95.082714,39.516712],[-94.876344,39.806894],[-95.382957,40.027112],[-95.731179,40.525436],[-91.785916,40.611488],[-91.375746,40.391879],[-91.406202,40.542698],[-91.123928,40.669152],[-90.952233,40.954047],[-91.100829,41.230532],[-91.05158,41.385283],[-90.364128,41.579633],[-90.153362,41.915593],[-90.206369,42.1455],[-90.700095,42.622461],[-91.072447,42.787732],[-91.175193,43.103771],[-91.079278,43.228259],[-91.31531,43.881808],[-91.970266,44.365842],[-92.787906,44.737432],[-92.802056,45.057423],[-92.650422,45.398507],[-92.883987,45.65487],[-92.683924,45.903939],[-92.319329,46.069289],[-92.291647,46.604649],[-92.178891,46.716741],[-91.781928,46.697604],[-90.880358,46.957661],[-90.78804,46.844886],[-90.920813,46.637432],[-90.327548,46.550262],[-89.929158,46.29975],[-88.141001,45.930608],[-88.13364,45.823128],[-87.831442,45.714938],[-87.887828,45.358122],[-87.647454,45.345232],[-87.72796,45.207956],[-87.59188,45.094689],[-87.983065,44.72073],[-87.970702,44.530292],[-87.021088,45.296541],[-87.048406,45.094259],[-87.467089,44.553557],[-87.910172,43.236634],[-87.779527,42.732482],[-87.812461,42.232278],[-87.365439,41.629536],[-86.824828,41.76024],[-83.504334,41.731547],[-82.513827,41.384257],[-81.69325,41.514161],[-80.533774,41.973475],[-80.519342,39.721403],[-79.476662,39.721078],[-79.412051,39.240546],[-78.795857,39.606934],[-78.474178,39.51624],[-78.143478,39.690412],[-77.853436,39.607117],[-77.759315,39.345314],[-77.058254,38.880069],[-77.286202,38.347025],[-77.024866,38.386791],[-76.910832,38.197073],[-76.265998,37.91138],[-76.339892,37.655966],[-76.722156,37.83668],[-76.252415,37.447274],[-76.475927,37.250543],[-76.300352,37.00885],[-76.780532,37.209336],[-76.482407,36.917364],[-76.058154,36.916947],[-75.867044,36.550754],[-81.680137,36.585518],[-81.718282,36.350388],[-82.02664,36.130222],[-82.325169,36.119363],[-82.531292,35.972188],[-82.701065,36.034404],[-82.955751,35.809802],[-83.880074,35.518745],[-84.052612,35.269982],[-84.28252,35.227877],[-84.321869,34.988408],[-85.605165,34.984678]]],[[[-75.210876,39.865709],[-74.760605,40.198909],[-75.191059,40.637971],[-75.110595,41.002174],[-74.821884,41.293838],[-74.641544,41.332879],[-73.91188,41.001297],[-74.246237,40.520963],[-73.971381,40.371709],[-74.090945,39.799978],[-74.850748,38.954538],[-74.933571,38.928519],[-74.905181,39.174945],[-75.165979,39.201842],[-75.542894,39.470447],[-75.466263,39.750737],[-75.210876,39.865709]]],[[[-75.242266,38.027209],[-75.962596,37.117535],[-75.981624,37.434116],[-75.712065,37.936082],[-75.242266,38.027209]]],[[[-86.880572,45.331467],[-86.956192,45.351179],[-86.82177,45.427602],[-86.880572,45.331467]]]]},\"properties\":{\"name\":\"Alabama\",\"nation\":\"USA \"}}]}","volume":"11","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":926480,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"De La Cruz, Jesse L.","contributorId":343174,"corporation":false,"usgs":false,"family":"De La Cruz","given":"Jesse","email":"","middleInitial":"L.","affiliations":[{"id":81893,"text":"Virginia Polytechnic and State University","active":true,"usgs":false}],"preferred":false,"id":926482,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Thorne, Emily D.","contributorId":342150,"corporation":false,"usgs":false,"family":"Thorne","given":"Emily D.","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":926481,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Silvis, Alexander","contributorId":264896,"corporation":false,"usgs":false,"family":"Silvis","given":"Alexander","affiliations":[{"id":54472,"text":"RES Inc.","active":true,"usgs":false}],"preferred":false,"id":926483,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Armstrong, Michael P.","contributorId":342608,"corporation":false,"usgs":false,"family":"Armstrong","given":"Michael P.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":926485,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"King, R. Andrew","contributorId":342609,"corporation":false,"usgs":false,"family":"King","given":"R.","email":"","middleInitial":"Andrew","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":926486,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70264589,"text":"70264589 - 2024 - Surface elevation trends in North Carolina's coastal wetlands","interactions":[],"lastModifiedDate":"2025-03-17T14:31:11.336488","indexId":"70264589","displayToPublicDate":"2024-03-01T09:22:04","publicationYear":"2024","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"title":"Surface elevation trends in North Carolina's coastal wetlands","docAbstract":"Coastal wetlands exist in the narrow fringe between uplands and open water and consequently, are uniquely vulnerable to the impacts of sea level rise (SLR). Predictive models suggest that in the coming decades sea level rise will lead to substantial losses of coastal wetland habitat in North Carolina. Empirical measurements of wetland response to SLR are vital for understanding which wetland complexes are most immediately threatened, so that mitigation, adaptation, and conservation efforts can be prioritized accordingly. Surface Elevation Tables (SETs) provide high resolution measures of wetland elevation change that can meet this need.
The North Carolina SET Community of Practice (NC SET COP) is a voluntary and unfunded partnership among stakeholders who have either installed Surface Elevation Tables (SETs) in North Carolina coastal wetlands, or who rely on SET data. The NC SET COP was formed in 2018 to identify and map the locations of all SETs installed within North Carolina, increase collaboration among SET users, and support efforts to identify regional trends in North Carolina coastal wetland response to sea level rise. The data presented here represent the first synoptic analysis of elevation trend data collected by NC SET COP partners.
Of 132 SETs installed across North Carolina (NC), 33 recorded net losses in elevation over the entire record of measurement. Among the 99 SETs that recorded positive elevation change, 79 (80%) did not build elevation fast enough to keep pace with the average rate of SLR over the past 30 years. The story these data tell is clear: the majority of NC’s coastal wetlands are not keeping pace with SLR. These data also provide a spatially explicit understanding of which wetlands are most at risk, and as a result, the SET data can help guide the use of restoration efforts for maximum effectiveness.
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